To see the other types of publications on this topic, follow the link: Image Clustering.
Dissertations / Theses on the topic 'Image Clustering'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Image Clustering.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
U, Leong Hou. "Web image clustering and retrieval." Thesis, University of Macau, 2005. http://umaclib3.umac.mo/record=b1445902.
Full text
2
Sayar, Ahmet. "Image Annotation With Semi-supervised Clustering." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12611251/index.pdf.
Full textAbstract:
Image annotation is defined as generating a set of textual words for a given image, learning from the available training data consisting of visual image content and annotation words.
Methods developed for image annotation usually make use of region clustering algorithms to quantize the visual information. Visual codebooks are generated from the region clusters of low level visual features. These codebooks are then, matched with the words of the text document related to the image, in various ways.
In this thesis, we propose a new image annotation technique, which improves the representation and quantization of the visual information by employing the available but unused information, called side information, which is hidden in the system. This side information is used to semi-supervise the clustering process which creates the visterms. The selection of side information depends on the visual image content, the annotation words and the relationship between them. Although there may be many different ways of defining and selecting side information, in this thesis, three types of side information are proposed. The first one is the hidden topic probability information obtained automatically from the text document associated with the image. The second one is the orientation and the third one is the color information around interest points that correspond to critical locations in the image. The side information provides a set of constraints in a semi-supervised K-means region clustering algorithm. Consequently, in generation of the visual terms from the regions, not only low level features are clustered, but also side information is used to complement the visual information,
called visterms. This complementary information is expected to close the semantic gap between the low level features extracted from each region and the high level textual information. Therefore, a better match between visual codebook and the annotation words is obtained. Moreover, a speedup is obtained in the modified K-means algorithm because of the constraints brought by the side information. The proposed algorithm is implemented in a high performance parallel computation environment.
3
Spång, Anton. "Automatic Image Annotation by Sharing Labels Based on Image Clustering." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210164.
Full textAbstract:
The growth of image collection sizes during the development has currently made manual annotation unfeasible, leading to the need for accurate and time efficient image annotation methods. This project evaluates a system for Automatic Image Annotation to see if it is possible to share annotations between images based on un-supervised clustering. The evaluation of the system included performing experiments with different algorithms and different unlabeled data sets. The system is also compared to an award winning Convolutional Neural Network model, used as a baseline, to see if the system’s precision and/or recall could be better than the baseline model’s. The results of the experiment conducted in this work showed that the precision and recall could be increased on the data used in this thesis, an increase of 0.094 in precision and 0.049 in recall in average for the system compared to the baseline.Utvecklingen av bildkollektioners storlekar har fram till idag ökat behovet av ett pålitligt och effektivt annoteringsverktyg i och med att manuell annotering har blivit ineffektivt. Denna rapport utvärderar möjligheterna att dela bildtaggar mellan visuellt lika bilder med ett system för automatisk bildannotering baserat på klustring. Utvärderingen sker i form av flera experiment med olika algoritmer och olika omärkta datamängder. I experimenten är systemet jämfört med en prisbelönt konvolutionell neural nätverksmodell, vilken är använd som utgångspunkt, för att undersöka om systemets resultat kan bli bättre än utgångspunktens resultat. Resultaten visar att både precisionen och återkallelsen förbättrades i de experiment som genomfördes på den data använd i detta arbete. En precisionsökning med 0.094 och en återkallelseökning med 0.049 för det implementerade systemet jämfört med utgångspunkten, över det genomförda experimenten.
4
Chang, Soong Uk. "Clustering with mixed variables /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe19086.pdf.
Full text
5
Daniels, Kristine Jean. "Clustering of Database Query Results." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1282.pdf.
Full text
6
Ekstrom, Nathan Hyrum. "Increasing DOGMA Scaling Through Clustering." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2359.pdf.
Full text
7
Kong, Tian Fook. "Multilevel spectral clustering : graph partitions and image segmentation." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45275.
Full textAbstract:
Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008.Includes bibliographical references (p. 145-146).
While the spectral graph partitioning method gives high quality segmentation, segmenting large graphs by the spectral method is computationally expensive. Numerous multilevel graph partitioning algorithms are proposed to reduce the segmentation time for the spectral partition of large graphs. However, the greedy local refinement used in these multilevel schemes has the tendency of trapping the partition in poor local minima. In this thesis, I develop a multilevel graph partitioning algorithm that incorporates the inverse powering method with greedy local refinement. The combination of the inverse powering method with greedy local refinement ensures that the partition quality of the multilevel method is as good as, if not better than, segmenting the large graph by the spectral method. In addition, I present a scheme to construct the adjacency matrix, W and degree matrix, D for the coarse graphs. The proposed multilevel graph partitioning algorithm is able to bisect a graph (k = 2) with significantly shorter time than segmenting the original graph without the multilevel implementation, and at the same time achieving the same normalized cut (Ncut) value. The starting eigenvector, obtained by solving a generalized eigenvalue problem on the coarsest graph, is close to the Fiedler vector of the original graph. Hence, the inverse iteration needs only a few iterations to converge the starting vector. In the k-way multilevel graph partition, the larger the graph, the greater the reduction in the time needed for segmenting the graph. For the multilevel image segmentation, the multilevel scheme is able to give better segmentation than segmenting the original image. The multilevel scheme has higher success of preserving the salient part of an object.
(cont.) In this work, I also show that the Ncut value is not the ultimate yardstick for the segmentation quality of an image. Finding a partition that has lower Ncut value does not necessary means better segmentation quality. Segmenting large images by the multilevel method offers both speed and quality.
by Tian Fook Kong.
S.M.
8
Fang, Yan. "Data clustering and graph-based image matching methods." Thesis, University of York, 2012. http://etheses.whiterose.ac.uk/4778/.
Full textAbstract:
This thesis describes our novel methods for data clustering, graph characterizing and image matching. In Chapter 3, our main contribution is the M1NN agglomerative clustering method with a new parallel merging algorithm. A cluster characterizing quantity is derived from the path-based dissimilarity measure. In Chapter 4, our main contribution is the modified log likelihood model for quantitative clustering analysis. The energy of a graph is adopted to define the description length to measure the complexity of a clustering. In Chapter 5, our main contribution is an image matching method based on Delaunay graph characterization and node selection. A normalized Euclidean distance on Delaunay graphs is found useful to estimate pairwise distances.
9
Davis, Aaron Samuel. "Bisecting Document Clustering Using Model-Based Methods /." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3332.pdf.
Full text
10
Piatrik, Tomas. "Image clustering and Video Summarisation using ant-inspired methods." Thesis, University of London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.509746.
Full text
11
Jan, Ying-Wei. "Segmentation and clustering in neural networks for image recognition." Ohio : Ohio University, 1994. http://www.ohiolink.edu/etd/view.cgi?ohiou1177102152.
Full text
12
Alfraih, Areej S. "Feature extraction and clustering techniques for digital image forensics." Thesis, University of Surrey, 2015. http://epubs.surrey.ac.uk/808306/.
Full textAbstract:
This thesis proposes an adaptive algorithm which applies feature extraction and clustering techniques for cloning detection and localization in digital images. Multiple contributions have been made to test the performance of different feature detectors for forensic use. The �first contribution is to improve a previously published algorithm by Wang et al. by localizing tampered regions using the grey-level co-occurrence matrix (GLCM) for extracting texture features from the chromatic component of an image (Cb or Cr component). The main trade-off� is a diminishing detection accuracy as the region size decreases. The second contribution is based on extracting Maximally Stable Extremal Regions (MSER) features for cloning detection, followed by k-means clustering for cloning localization. Then, for comparison purposes, we implement the same approach using Speeded Up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT). Experimental results show that we can detect and localize cloning in tampered images with an accuracy reaching 97% using MSER features. The usability and effi�cacy of our approach is verified by comparing with recent state-of-the-art approaches. For the third contribution we propose a flexible methodology for detecting cloning in images, based on the use of feature detectors. We determine whether a particular match is the result of a cloning event by clustering the matches using k-means clustering and using a Support Vector Machine (SVM) to classify the clusters. This descriptor-agnostic approach allows us to combine the results of multiple feature descriptors, increasing the potential number of keypoints in the cloned region. Results using MSER, SURF and SIFT outperform state of the art where the highest true positive rate is achieved at approximately 99.60% and the false positive rate is achieved at 1.6%, when different descriptors are combined. A statistical �filtering step, based on computing the median value of the dissimilarity matrix, is also proposed. Moreover, our algorithm uses an adaptive technique for selecting the optimal k value for each image independently, allowing our method to detect multiple cloned regions. Finally, we propose an adaptive technique that chooses feature detectors based on the type of image being tested. Some detectors are robust in detecting features in textured images while other detectors are robust in detecting features in smooth images. Combining the detectors makes them complementary to each other and can generate optimal results. The highest value for the area under ROC curve is achieved at approximately 98.87%. We also test the performance of agglomerative hierarchical clustering for cloning localization. Hierarchical and k-means clustering techniques have a similar performance for cloning localization. The True Positive Rate (TPR) for match level localization is achieved at approximately 97.59% and 96.43% for k-means and hierarchical clustering techniques, respectively. The robustness of our technique is analyzed against additive white Gaussian noise and JPEG compression. Our technique is still reliable even when using a low signal-to-noise (SNR = 20 dB) or a low JPEG compression quality factor (QF = 50).
13
Casaca, Wallace Correa de Oliveira. "Graph Laplacian for spectral clustering and seeded image segmentation." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-24062015-112215/.
Full textAbstract:
Image segmentation is an essential tool to enhance the ability of computer systems to efficiently perform elementary cognitive tasks such as detection, recognition and tracking. In this thesis we concentrate on the investigation of two fundamental topics in the context of image segmentation: spectral clustering and seeded image segmentation. We introduce two new algorithms for those topics that, in summary, rely on Laplacian-based operators, spectral graph theory, and minimization of energy functionals. The effectiveness of both segmentation algorithms is verified by visually evaluating the resulting partitions against state-of-the-art methods as well as through a variety of quantitative measures typically employed as benchmark by the image segmentation community. Our spectral-based segmentation algorithm combines image decomposition, similarity metrics, and spectral graph theory into a concise and powerful framework. An image decomposition is performed to split the input image into texture and cartoon components. Then, an affinity graph is generated and weights are assigned to the edges of the graph according to a gradient-based inner-product function. From the eigenstructure of the affinity graph, the image is partitioned through the spectral cut of the underlying graph. Moreover, the image partitioning can be improved by changing the graph weights by sketching interactively. Visual and numerical evaluation were conducted against representative spectral-based segmentation techniques using boundary and partition quality measures in the well-known BSDS dataset. Unlike most existing seed-based methods that rely on complex mathematical formulations that typically do not guarantee unique solution for the segmentation problem while still being prone to be trapped in local minima, our segmentation approach is mathematically simple to formulate, easy-to-implement, and it guarantees to produce a unique solution. Moreover, the formulation holds an anisotropic behavior, that is, pixels sharing similar attributes are preserved closer to each other while big discontinuities are naturally imposed on the boundary between image regions, thus ensuring better fitting on object boundaries. We show that the proposed approach significantly outperforms competing techniques both quantitatively as well as qualitatively, using the classical GrabCut dataset from Microsoft as a benchmark. While most of this research concentrates on the particular problem of segmenting an image, we also develop two new techniques to address the problem of image inpainting and photo colorization. Both methods couple the developed segmentation tools with other computer vision approaches in order to operate properly.Segmentar uma image é visto nos dias de hoje como uma prerrogativa para melhorar a capacidade de sistemas de computador para realizar tarefas complexas de natureza cognitiva tais como detecção de objetos, reconhecimento de padrões e monitoramento de alvos. Esta pesquisa de doutorado visa estudar dois temas de fundamental importância no contexto de segmentação de imagens: clusterização espectral e segmentação interativa de imagens. Foram propostos dois novos algoritmos de segmentação dentro das linhas supracitadas, os quais se baseiam em operadores do Laplaciano, teoria espectral de grafos e na minimização de funcionais de energia. A eficácia de ambos os algoritmos pode ser constatada através de avaliações visuais das segmentações originadas, como também através de medidas quantitativas computadas com base nos resultados obtidos por técnicas do estado-da-arte em segmentação de imagens. Nosso primeiro algoritmo de segmentação, o qual ´e baseado na teoria espectral de grafos, combina técnicas de decomposição de imagens e medidas de similaridade em grafos em uma única e robusta ferramenta computacional. Primeiramente, um método de decomposição de imagens é aplicado para dividir a imagem alvo em duas componentes: textura e cartoon. Em seguida, um grafo de afinidade é gerado e pesos são atribuídos às suas arestas de acordo com uma função escalar proveniente de um operador de produto interno. Com base no grafo de afinidade, a imagem é então subdividida por meio do processo de corte espectral. Além disso, o resultado da segmentação pode ser refinado de forma interativa, mudando-se, desta forma, os pesos do grafo base. Experimentos visuais e numéricos foram conduzidos tomando-se por base métodos representativos do estado-da-arte e a clássica base de dados BSDS a fim de averiguar a eficiência da metodologia proposta. Ao contrário de grande parte dos métodos existentes de segmentação interativa, os quais são modelados por formulações matemáticas complexas que normalmente não garantem solução única para o problema de segmentação, nossa segunda metodologia aqui proposta é matematicamente simples de ser interpretada, fácil de implementar e ainda garante unicidade de solução. Além disso, o método proposto possui um comportamento anisotrópico, ou seja, pixels semelhantes são preservados mais próximos uns dos outros enquanto descontinuidades bruscas são impostas entre regiões da imagem onde as bordas são mais salientes. Como no caso anterior, foram realizadas diversas avaliações qualitativas e quantitativas envolvendo nossa técnica e métodos do estado-da-arte, tomando-se como referência a base de dados GrabCut da Microsoft. Enquanto a maior parte desta pesquisa de doutorado concentra-se no problema específico de segmentar imagens, como conteúdo complementar de pesquisa foram propostas duas novas técnicas para tratar o problema de retoque digital e colorização de imagens.
14
Collings, Jared M. "Clustering Methods for Delineating Regions of Spatial Stationarity." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2175.pdf.
Full text
15
Beheshti, Maedeh. "Segmentation, Feature Extraction & Autoimmune Clustering for Foreground-background Image Retrieval." Thesis, Griffith University, 2017. http://hdl.handle.net/10072/365378.
Full textAbstract:
In our digital era, many attempts in remote sensing, fashion, crime prevention, publishing, medicine, architecture and bio-medicine have resulted in a large number of image data sets. Traditional methods to search and retrieve from these data sets are gradually being replaced by state-of-the-art and modern techniques such as content based image retrieval. Retrieving images through extracting contents as a feature and a similarity measure is one of the most challenging applications of computer vision. Due to the increasing number of images with different varieties and types, the traditional content based image retrieval systems are unable to properly exploit the content information of images for retrieval. Thus, extracting relevant features of images and finding a measure of image similarity that returns appropriate relationships is challenging. Content Based Image Retrieval (CBIR) is one of the open problems which still needs much more research effort to completely replace traditional retrieval systems. Feature extraction based on colour, texture, shape and etc. which has been done locally or globally for an image is one of the main parts of CBIR. Image segmentation, which extracts objects from the background and partitioning an image into several regions, helps facilitate feature extraction based on shapes or region of interest (ROI).Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of IInformation and Communication Technology
Science, Environment, Engineering and Technology
Full Text
16
Espinosa, Javier. "Clustering of Image Search Results to Support Historical Document Recognition." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5577.
Full textAbstract:
Context. Image searching in historical handwritten documents is a challenging problem in computer vision and pattern recognition. The amount of documents which have been digitalized is increasing each day, and the task to find occurrences of a selected sub-image in a collection of documents has special interest for historians and genealogist. Objectives. This thesis develops a technique for image searching in historical documents. Divided in three phases, first the document is segmented into sub-images according to the words on it. These sub-images are defined by a features vector with measurable attributes of its content. And based on these vectors, a clustering algorithm computes the distance between vectors to decide which images match with the selected by the user. Methods. The research methodology is experimentation. A quasi-experiment is designed based on repeated measures over a single group of data. The image processing, features selection, and clustering approach are the independent variables; whereas the accuracies measurements are the dependent variable. This design provides a measurement net based on a set of outcomes related to each other. Results. The statistical analysis is based on the F1 score to measure the accuracy of the experimental results. This test analyses the accuracy of the experiment regarding to its true positives, false positives, and false negatives detected. The average F-measure for the experiment conducted is F1 = 0.59, whereas the actual performance value of the method is matching ratio of 66.4%. Conclusions. This thesis provides a starting point in order to develop a search engine for historical document collections based on pattern recognition. The main research findings are focused in image enhancement and segmentation for degraded documents, and image matching based on features definition and cluster analysis.
17
Shihab, Ahmed Ismail. "Fuzzy clustering algorithms and their application to medical image analysis." Thesis, Imperial College London, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.271556.
Full text
18
CHEN, SHANGYE. "ENHANCING FUZZY CLUSTERING METHODS FOR IMAGE SEGMENTATION USING SPATIAL INFORMATION." Miami University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=miami1556555486273.
Full text
19
Shojanazeri, Hamid. "A new perceptual dissimilarity measure for image retrieval and clustering." Thesis, Federation University Australia, 2018. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/168086.
Full textAbstract:
Image retrieval and clustering are two important tools for analysing and organising images. Dissimilarity measure is central to both image retrieval and clustering. The performance of image retrieval and clustering algorithms depends on the effectiveness of the dissimilarity measure. ‘Minkowski’ distance, or more specifically, ‘Euclidean’ distance, is the most widely used dissimilarity measure in image retrieval and clustering. Euclidean distance depends only on the geometric position of two data instances in the feature space and completely ignores the data distribution. However, data distribution has an effect on human perception. The argument that two data instances in a dense area are more perceptually dissimilar than the same two instances in a sparser area, is proposed by psychologists. Based on this idea, a dissimilarity measure called, ‘mp’, has been proposed to address Euclidean distance’s limitation of ignoring the data distribution. Here, mp relies on data distribution to calculate the dissimilarity between two instances. As prescribed in mp, higher data mass between two data instances implies higher dissimilarity, and vice versa. mp relies only on data distribution and completely ignores the geometric distance in its calculations. In the aggregation of dissimilarities between two instances over all the dimensions in feature space, both Euclidean distance and mp give same priority to all the dimensions. This may result in a situation that the final dissimilarity between two data instances is determined by a few dimensions of feature vectors with relatively much higher values. As a result, the dissimilarity derived may not align well with human perception. The need to address the limitations of Minkowski distance measures, along with the importance of a dissimilarity measure that considers both geometric distance and the perceptual effect of data distribution in measuring dissimilarity between images motivated this thesis. It studies the performance of mp for image retrieval. It investigates a new dissimilarity measure that combines both Euclidean distance and data distribution. In addition to these, it studies the performance of such a dissimilarity measure for image retrieval and clustering. Our performance study of mp for image retrieval shows that relying only on data distribution to measure the dissimilarity results in some situations, where the mp’s measurement is contrary to human perception. This thesis introduces a new dissimilarity measure called, perceptual dissimilarity measure (PDM). PDM considers the perceptual effect of data distribution in combination with Euclidean distance. PDM has two variants, PDM1 and PDM2. PDM1 focuses on improving mp by weighting it using Euclidean distance in situations where mp may not retrieve accurate results. PDM2 considers the effect of data distribution on the perceived dissimilarity measured by Euclidean distance. PDM2 proposes a weighting system for Euclidean distance using a logarithmic transform of data mass. The proposed PDM variants have been used as alternatives to Euclidean distance and mp to improve the accuracy in image retrieval. Our results show that PDM2 has consistently performed the best, compared to Euclidean distance, mp and PDM1. PDM1’s performance was not consistent, although it has performed better than mp in all the experiments, but it could not outperform Euclidean distance in some cases. Following the promising results of PDM2 in image retrieval, we have studied its performance for image clustering. k-means is the most widely used clustering algorithm in scientific and industrial applications. k-medoids is the closest clustering algorithm to k-means. Unlike k-means which works only with Euclidean distance, k-medoids gives the option to choose the arbitrary dissimilarity measure. We have used Euclidean distance, mp and PDM2 as the dissimilarity measure in k-medoids and compared the results with k-means. Our clustering results show that PDM2 has perfromed overally the best. This confirms our retrieval results and identifies PDM2 as a suitable dissimilarity measure for image retrieval and clustering.Doctor of Philosophy
20
Inkiläinen, V. (Valtteri). "Clustering image sets with features from deep convolutional neural networks." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201910313044.
Full textAbstract:
Abstract. This thesis compares the results of clustering image sets by features extracted using different layers of a convolutional neural network. The image features were extracted with layers of a pre-trained image classification network which layer weights were trained with ImageNet dataset. Eight image sets were used to test which extracted features achieve the best clustering accuracies. Features from the test image sets were extracted with the layers of the network architecture, and the features were clustered on a layer by layer basis. The clustering accuracies were measured with normalized mutual information (NMI).
The results show that the clustering accuracies depend on the characteristic of the image set being clustered. The image sets with more than two image categories had the best NMI scores with the features from the second last layer in the architecture, while the image sets with two categories had different layers give the best NMI scores. Moreover, the image set with blurred images had the best result come from few of the first layers, showing that the current method of selecting the second last layer for feature extraction in pre-trained CNNs is not always optimal.Piirteiden vaikutus kuvaryhmän klusterointiin käyttäen konvoluutioverkolla irroitettuja piirteitä. Tiivistelmä. Tässä työssä vertaillaan kuvajoukkojen klusterointituloksia eri piirteillä. Piirteiden irrotukseen kuvista käytettiin valmiiksi koulutetun konvoluutio neuroverkon eri tasoja. Neuroverkko oli koulutettu kuvaluokitteluun ImageNet datajoukolla. Kahdeksan kuvajoukkoa klusteroitiin eri piirteillä, jotka oli irrotettu neuroverkon eri tasoilla. Näiden kuvajoukkojen klusterointitarkkuus mitattiin parhaan piirreirrotus tason löytämiseksi kullekin kuvajoukolle. Klusteroinnin tulos mitattiin normalisoidulla yhteisen informaation metriikalla (normalized mutual information).
Työn tulos osoitti, että klusterointitulos taso tasolta mitatessa riippuu klusteroitavasta kuvajoukosta. Kuvajoukot, jotka sisälsivät kuvia useammasta kuin kahdesta kategoriasta, klusteroituvat parhaiten verkon toiseksi viimeisellä tasolla irrotetuilla piirteillä. Kahden kategorian kuvajoukkojen parhaat klusterointi tulokset tulivat eri tasoilla. Kuvajoukko joka sisälsi kuvia sumeista ja tarkoista kuvista, saavutti parhaat klusterointitulokset piirteillä, jotka oli irrotettu verkon ylemmiltä tasoilta. Tulokset osoittavat, että yleisesti käytetty menetelmä valita valmiiksi koulutetun verkon toiseksi viimeinen taso piirreirrotukseen ei aina anna optimaalista tulosta.
21
Kučerová, Dana. "Marketingový význam body image." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-16626.
Full text
22
Wang, Yu Long. "Atomic representation for subspace clustering and pattern classification." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691898.
Full text
23
Ndebele, Nothando Elizabeth. "Clustering algorithms and their effect on edge preservation in image compression." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1008210.
Full textAbstract:
Image compression aims to reduce the amount of data that is stored or transmitted for images. One technique that may be used to this end is vector quantization. Vectors may be used to represent images. Vector quantization reduces the number of vectors required for an image by representing a cluster of similar vectors by one typical vector that is part of a set of vectors referred to as the code book. For compression, for each image vector, only the closest codebook vector is stored or transmitted. For reconstruction, the image vectors are again replaced by the the closest codebook vectors. Hence vector quantization is a lossy compression technique and the quality of the reconstructed image depends strongly on the quality of the codebook. The design of the codebook is therefore an important part of the process. In this thesis we examine three clustering algorithms which can be used for codebook design in image compression: c-means (CM), fuzzy c-means (FCM) and learning vector quantization (LVQ). We give a description of these algorithms and their application to codebook design. Edges are an important part of the visual information contained in an image. It is essential therefore to use codebooks which allow an accurate representation of the edges. One of the shortcomings of using vector quantization is poor edge representation. We therefore carry out experiments using these algorithms to compare their edge preserving qualities. We also investigate the combination of these algorithms with classified vector quantization (CVQ) and the replication method (RM). Both these methods have been suggested as methods for improving edge representation. We use a cross validation approach to estimate the mean squared error to measure the performance of each of the algorithms and the edge preserving methods. The results reflect that the edges are less accurately represented than the non - edge areas when using CM, FCM and LVQ. The advantage of using CVQ is that the time taken for code book design is reduced particularly for CM and FCM. RM is found to be effective where the codebook is trained using a set that has larger proportions of edges than the test set.
24
Moreno, José G. "Text-Based Ephemeral Clustering for Web Image Retrieval on Mobile Devices." Caen, 2014. http://www.theses.fr/2014CAEN2036.
Full textAbstract:
Dans cette thèse, nous présentons une étude sur la visualisation des résultats Web d'images sur les dispositifs nomades. Nos principales conclusions ont été inspirées par les avancées récentes dans deux principaux domaines de recherche – la recherche d'information et le traitement automatique du langage naturel. Tout d’abord, nous avons examiné différents sujets tels que le regroupement des résultats Web, les interfaces mobiles, la fouille des intentions sur une requête, pour n'en nommer que quelques-uns. Ensuite, nous nous sommes concentré sur les mesures d'association lexical, les métriques de similarité d'ordre élevé, etc. Notamment afin de valider notre hypothèse, nous avons réalisé différentes expériences avec des jeux de données spécifiques de la tâche. De nombreuses caractéristiques sont évaluées dans les solutions proposées. Premièrement, la qualité de regroupement en utilisant à la fois des métriques d'évaluation classiques, mais aussi des métriques plus récentes. Deuxièmement, la qualité de l'étiquetage de chaque groupe de documents est évaluée pour s'assurer au maximum que toutes les intentions des requêtes sont couvertes. Finalement, nous évaluons l'effort de l'utilisateur à explorer les images dans une interface basée sur l'utilisation des galeries présentées sur des dispositifs nomades. Un chapitre entier est consacré à chacun de ces trois aspects dans lesquels les jeux de données - certains d'entre eux construits pour évaluer des caractéristiques spécifiques - sont présentés. Comme résultats de cette thèse, nous sommes développés : deux algorithmes adaptés aux caractéristiques du problème, deux jeux de données pour les tâches respectives et un outil d'évaluation pour le regroupement des résultats d'une requête (SRC pour les sigles en anglais). Concernant les algorithmes, Dual C-means est notre principal contribution. Il peut être vu comme une généralisation de notre algorithme développé précédemment, l'AGK-means. Les deux sont basés sur des mesures d'association lexical à partir des résultats Web. Un nouveau jeu de données pour l'évaluation complète d'algorithmes SRC est élaboré et présenté. De même, un nouvel ensemble de données sur les images Web est développé et utilisé avec une nouvelle métrique à fin d'évaluer l'effort fait pour les utilisateurs lors qu'ils explorent un ensemble d'images. Enfin, nous avons développé un outil d'évaluation pour le problème SRC, dans lequel nous avons mis en place plusieurs mesures classiques et récentes utilisées en SRC. Nos conclusions sont tirées compte tenu des nombreux facteurs qui ont été discutés dans cette thèse. Cependant, motivés par nos conclusions, des études supplémentaires pourraient être développés. Celles-ci sont discutées à la fin de ce manuscrit et notre résultats préliminaires suggère que l’association de plusieurs sources d'information améliore déjà la qualité du regroupementIn this thesis, we present a study about Web image results visualization on mobile devices. Our main findings were inspired by the recent advances in two main research areas - Information Retrieval and Natural Language Processing. In the former, we considered different topics such as search results clustering, Web mobile interfaces, query intent mining, to name but a few. In the latter, we were more focused in collocation measures, high order similarity metrics, etc. Particularly in order to validate our hypothesis, we performed a great deal of different experiments with task specific datasets. Many characteristics are evaluated in the proposed solutions. First, the clustering quality in which classical and recent evaluation metrics are considered. Secondly, the labeling quality of each cluster is evaluated to make sure that all possible query intents are covered. Thirdly and finally, we evaluate the user's effort in exploring the images in a gallery-based interface. An entire chapter is dedicated to each of these three aspects in which the datasets - some of them built to evaluate specific characteristics - are presented. For the final results, we can take into account two developed algorithms, two datasets and a SRC evaluation tool. From the algorithms, Dual C-means is our main product. It can be seen as a generalization of our previously developed algorithm, the AGK-means. Both are based in text-based similarity metrics. A new dataset for a complete evaluation of SRC algorithms is developed and presented. Similarly, a new Web image dataset is developed and used together with a new metric to measure the users effort when a set of Web images is explored. Finally, we developed an evaluation tool for the SRC problem, in which we have implemented several classical and recent SRC metrics. Our conclusions are drawn considering the numerous factors that were discussed in this thesis. However, additional studies could be motivated based in our findings. Some of them are discussed in the end of this study and preliminary analysis suggest that they are directions that have potential
25
Vosahlik, Jan. "Air void clustering in concrete." Thesis, Kansas State University, 2014. http://hdl.handle.net/2097/18206.
Full textAbstract:
Master of ScienceDepartment of Civil Engineering
Kyle A. Riding
Air void clustering around coarse aggregate in concrete has been identified as a potential source of low strengths in concrete mixes by several Departments of Transportation around the country. Research was carried out to (1) develop a quantitative measure of air void clustering around aggregates, (2) investigate whether air void clustering can be reproduced in a laboratory environment, (3) determine if air void clustering can blamed for lower compressive strengths in concrete mixes, (4) and identify potential factors that may cause clustering. Five types of coarse aggregate and five different air entraining agents were included in the laboratory study to see if aggregate type or chemical composition of air entraining agent directly relates to air void clustering. A total of 65 mixes were made, implementing the frequently used technique of retempering that has been previously associated with air void clustering around aggregates. Compressive strength specimens as well as samples for hardened void analysis were made. Compressive strength at 7 and 28 days was determined and the automated hardened void analysis (including a new method of clustering evaluation) was performed on all samples. It was found that it is possible to reproduce air void clustering in laboratory conditions. However, the results have shown that retempering does not always cause air void clustering. It was also observed that air void clustering is not responsible for a decrease in compressive strength of retempered concrete as neither aggregate type nor chemical composition of air entraining agent had a significant impact on severity of void clustering around coarse aggregate particles. It was also found that the total air content and an inhomogeneous microstructure and not air void clustering were responsible for lower strengths.
26
Chaganti, Shikha. "Image Analysis of Glioblastoma Histopathology." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406820611.
Full text
27
Islam, A. B. M. Rezbaul. "Skin Detection in Image and Video Founded in Clustering and Region Growing." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1538658/.
Full textAbstract:
Researchers have been involved for decades in search of an efficient skin detection method. Yet current methods have not overcome the major limitations. To overcome these limitations, in this dissertation, a clustering and region growing based skin detection method is proposed. These methods together with a significant insight result in a more effective algorithm. The insight concerns a capability to define dynamically the number of clusters in a collection of pixels organized as an image. In clustering for most problem domains, the number of clusters is fixed a priori and does not perform effectively over a wide variety of data contents. Therefore, in this dissertation, a skin detection method has been proposed using the above findings and validated. This method assigns the number of clusters based on image properties and ultimately allows freedom from manual thresholding or other manual operations. The dynamic determination of clustering outcomes allows for greater automation of skin detection when dealing with uncertain real-world conditions.
28
Bergholm, Marcus. "Clustering users based on the user’s photo library." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230901.
Full textAbstract:
For any user-adaptive system the most important task is to provide the users with what they want and need without them asking for it explicitly. This process can be called personalisation and is done by tailoring the service or product for individual users or user groups. In this thesis, we explore the possibilities to build a model that clusters users based on the user’s photo library. This was to create a better personalised experience within a service called Degoo. The model used to perform the clustering is called Deep Embedding Clustering and was evaluated on several internal indices alongside an automated categorization model to get an indication of what type of images the clusters had. The user clustering was later evaluated based on split-tests running within the Degoo service. The results shows that four out of five clusters had some general indication of types such as vacation photos, clothes, text, and people. The evaluation of the clustering impact on the split-tests shows that we could see patterns that indicated optimal attribute values for certain user clusters.Det ultimata målet för alla användaranpassade system är att ge användarna det som de behöver utan att de begär det explicit. Denna process kan kallas användaranpassning och görs genom att skräddarsy tjänsten eller produkten för enskilda användare eller användargrupper. I denna avhandling undersöker vi möjligheterna att bygga en modell som grupperar användare baserat på användarnas fotodata. Motivationen bakom detta var att skapa en bättre personlig upplevelse inom en tjänst som heter Degoo. Modellen som används för att utföra grupperingen heter Deep Embedding Clustering och utvärderades på flera interna index tillsammans med en automatiserad kategoriseringsmodell för att få en indikation av vilken typ av bilder grupperna hade. Användargrupperingen utvärderades senare baserat på flera split-test som körs inom Degoo tjänsten. Resultaten visar att fyra av fem grupper hade en allmän indikation på typer som semesterbilder, kläder, text och människor. Utvärderingen av grupperingseffekten på split-testerna visar att vi kunde se mönster som indikerar optimala attributvärden för vissa grupper.
29
Leung, Kam Shek Simon. "Image processing by region extraction using a clustering approach based on colour." Thesis, University of Stirling, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.281109.
Full text
30
Hasnat, Md Abul. "Unsupervised 3D image clustering and extension to joint color and depth segmentation." Thesis, Saint-Etienne, 2014. http://www.theses.fr/2014STET4013/document.
Full textAbstract:
L'accès aux séquences d'images 3D s'est aujourd'hui démocratisé, grâce aux récentes avancées dans le développement des capteurs de profondeur ainsi que des méthodes permettant de manipuler des informations 3D à partir d'images 2D. De ce fait, il y a une attente importante de la part de la communauté scientifique de la vision par ordinateur dans l'intégration de l'information 3D. En effet, des travaux de recherche ont montré que les performances de certaines applications pouvaient être améliorées en intégrant l'information 3D. Cependant, il reste des problèmes à résoudre pour l'analyse et la segmentation de scènes intérieures comme (a) comment l'information 3D peut-elle être exploitée au mieux ? et (b) quelle est la meilleure manière de prendre en compte de manière conjointe les informations couleur et 3D ? Nous abordons ces deux questions dans cette thèse et nous proposons de nouvelles méthodes non supervisées pour la classification d'images 3D et la segmentation prenant en compte de manière conjointe les informations de couleur et de profondeur. A cet effet, nous formulons l'hypothèse que les normales aux surfaces dans les images 3D sont des éléments à prendre en compte pour leur analyse, et leurs distributions sont modélisables à l'aide de lois de mélange. Nous utilisons la méthode dite « Bregman Soft Clustering » afin d'être efficace d'un point de vue calculatoire. De plus, nous étudions plusieurs lois de probabilités permettant de modéliser les distributions de directions : la loi de von Mises-Fisher et la loi de Watson. Les méthodes de classification « basées modèles » proposées sont ensuite validées en utilisant des données de synthèse puis nous montrons leur intérêt pour l'analyse des images 3D (ou de profondeur). Une nouvelle méthode de segmentation d'images couleur et profondeur, appelées aussi images RGB-D, exploitant conjointement la couleur, la position 3D, et la normale locale est alors développée par extension des précédentes méthodes et en introduisant une méthode statistique de fusion de régions « planes » à l'aide d'un graphe. Les résultats montrent que la méthode proposée donne des résultats au moins comparables aux méthodes de l'état de l'art tout en demandant moins de temps de calcul. De plus, elle ouvre des perspectives nouvelles pour la fusion non supervisée des informations de couleur et de géométrie. Nous sommes convaincus que les méthodes proposées dans cette thèse pourront être utilisées pour la classification d'autres types de données comme la parole, les données d'expression en génétique, etc. Elles devraient aussi permettre la réalisation de tâches complexes comme l'analyse conjointe de données contenant des images et de la paroleAccess to the 3D images at a reasonable frame rate is widespread now, thanks to the recent advances in low cost depth sensors as well as the efficient methods to compute 3D from 2D images. As a consequence, it is highly demanding to enhance the capability of existing computer vision applications by incorporating 3D information. Indeed, it has been demonstrated in numerous researches that the accuracy of different tasks increases by including 3D information as an additional feature. However, for the task of indoor scene analysis and segmentation, it remains several important issues, such as: (a) how the 3D information itself can be exploited? and (b) what is the best way to fuse color and 3D in an unsupervised manner? In this thesis, we address these issues and propose novel unsupervised methods for 3D image clustering and joint color and depth image segmentation. To this aim, we consider image normals as the prominent feature from 3D image and cluster them with methods based on finite statistical mixture models. We consider Bregman Soft Clustering method to ensure computationally efficient clustering. Moreover, we exploit several probability distributions from directional statistics, such as the von Mises-Fisher distribution and the Watson distribution. By combining these, we propose novel Model Based Clustering methods. We empirically validate these methods using synthetic data and then demonstrate their application for 3D/depth image analysis. Afterward, we extend these methods to segment synchronized 3D and color image, also called RGB-D image. To this aim, first we propose a statistical image generation model for RGB-D image. Then, we propose novel RGB-D segmentation method using a joint color-spatial-axial clustering and a statistical planar region merging method. Results show that, the proposed method is comparable with the state of the art methods and requires less computation time. Moreover, it opens interesting perspectives to fuse color and geometry in an unsupervised manner. We believe that the methods proposed in this thesis are equally applicable and extendable for clustering different types of data, such as speech, gene expressions, etc. Moreover, they can be used for complex tasks, such as joint image-speech data analysis
31
Kerwin, Matthew. "Comparison of Traditional Image Segmentation Techniques and Geostatistical Threshold." Thesis, James Cook University, 2006. https://eprints.qut.edu.au/99764/1/kerwin-honours-thesis.pdf.
Full textAbstract:
A general introduction to image segmentation is provided, including a detailed description of common classic techniques: Otsu’s threshold, k-means and fuzzy c-means clustering; and suggestions of ways in which these techniques have been subsequently modified for special situations.
Additionally, a relatively new approach is described, which attempts to address certain exposed failings of the classic techniques listed by incorporating a spatial statistical analysis technique commonly used in geological studies.
Results of different segmentation techniques are calculated for various images, and evaluated and compared, with deficiencies explained and suggestions for improvements made.
32
Davis, Nathan Scott. "An Analysis of Document Retrieval and Clustering Using an Effective Semantic Distance Measure." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2674.pdf.
Full text
33
Kang, Jung Won. "Effective temporal video segmentation and content-based audio-visual video clustering." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/13731.
Full text
34
Kéchichian, Razmig. "Structural priors for multiobject semi-automatic segmentation of three-dimensional medical images via clustering and graph cut algorithms." Phd thesis, INSA de Lyon, 2013. http://tel.archives-ouvertes.fr/tel-00967381.
Full textAbstract:
We develop a generic Graph Cut-based semiautomatic multiobject image segmentation method principally for use in routine medical applications ranging from tasks involving few objects in 2D images to fairly complex near whole-body 3D image segmentation. The flexible formulation of the method allows its straightforward adaption to a given application.\linebreak In particular, the graph-based vicinity prior model we propose, defined as shortest-path pairwise constraints on the object adjacency graph, can be easily reformulated to account for the spatial relationships between objects in a given problem instance. The segmentation algorithm can be tailored to the runtime requirements of the application and the online storage capacities of the computing platform by an efficient and controllable Voronoi tessellation clustering of the input image which achieves a good balance between cluster compactness and boundary adherence criteria. Qualitative and quantitative comprehensive evaluation and comparison with the standard Potts model confirm that the vicinity prior model brings significant improvements in the correct segmentation of distinct objects of identical intensity, the accurate placement of object boundaries and the robustness of segmentation with respect to clustering resolution. Comparative evaluation of the clustering method with competing ones confirms its benefits in terms of runtime and quality of produced partitions. Importantly, compared to voxel segmentation, the clustering step improves both overall runtime and memory footprint of the segmentation process up to an order of magnitude virtually without compromising the segmentation quality.
35
Akyama, Marcio Teruo. "Interpolação de imagens baseada em clustering." Universidade Tecnológica Federal do Paraná, 2010. http://repositorio.utfpr.edu.br/jspui/handle/1/916.
Full textAbstract:
O ato de executar zoom em imagens é uma tarefa que se aplica em diversas áreas que podem variar desde entretenimento até aplicações científicas. Um dos grandes desafios na área é manter a definição das bordas dos objetos da imagem sem que haja a criação de artefatos tais como aspecto serrilhado ou borramento. Diversos métodos de preservação de borda foram apresentados na literatura. Este trabalho apresenta a proposta de uma nova técnica de interpolação de imagens baseada em clustering que tem como objetivo aumentar a resolução da imagem em tons de cinza preservando as bordas dos objetos nela presentes com um método mais simples e de fácil implementação. Foram realizados testes da técnica proposta com diversas imagens de natureza diferente e seus resultados comparados aos métodos clássicos de interpolação de imagem encontrados na literatura. Para teste da eficácia foram consideradas a medida do PSNR e Correlação Cruzada com cada método comparado. Os resultados obtidos mostraram que a técnica é promissora e que cumpre os objetivos do projeto.Image zooming is a task applicable to many areas which can vary from entertainment to scientific applications. A big challenge is image edge preserving without creating artifacts like blurring or blocking. Several methods for edge preserving were proposed in literature. This work presents a new technique proposal based on clustering which aims to increase gray scale image resolution preserving objects edges with a simple method and easy to implement. Many different types of images were used to make tests of the proposed technique and results are compared to classical methods of image interpolation found in literature. PSNR and Cross-Correlation measurements were used to compare efficiency between methods. Results showed that the technique is quite competitive and meets the project goals.
36
Essa, Zahi. "Physical modelling of impurity diffusion and clustering phenomena in CMOS based image sensors." Phd thesis, Université Paul Sabatier - Toulouse III, 2013. http://tel.archives-ouvertes.fr/tel-01020497.
Full textAbstract:
L'essor de l'industrie micro-électronique au cours des dernières années n'aurait pas été possible sans les innovations en termes de procédés de fabrication de la technologie CMOS (Complementary Metal Oxide Semiconductor) induisant une amélioration continue des performances des composants. Ces innovations doivent relever les défis technologiques inhérents à la fois à la miniaturisation ainsi qu'à la diversification croissante des composants. En réponse à ces défis, des approches de modélisation de type TCAD (Technology Computer Aided Design), permettent de réduire nettement le temps et le coût de développement de ces nouvelles technologies. Dans ce cadre, cette thèse s'intéresse à l'élaboration de modèles TCAD permettant la prise en compte des différents mécanismes physiques ayant lieu lors de l'utilisation des procédés de fabrication avancés. Dans une première partie, les mécanismes de diffusion et d'activation pour des fortes doses d'implantation ont pu être étudiés notamment dans le cas de l'implantation plasma, technique très prometteuse pour des applications de dopage conforme dans les capteurs d'image ou transistors TriGates. La mise en évidence et la modélisation d'agrégats de bore-interstitiel de grande taille ont ainsi pu être menées pour des conditions de fort dopage. Dans une deuxième partie, la diffusion et le transfert d'espèces chimiques entre différents matériaux ont été évalués. Ainsi, la perte de dose de bore dans le silicium dans les empilements " espaceurs " ainsi que la diffusion de bore correspondante dans l'oxyde ont été étudiés. De même, l'évaluation de la diffusion du lanthane pendant un recuit thermique dans les empilements de grille avec oxyde à forte permittivité diélectrique (high-k) a pu être menée. En dernière partie, l'impact de ces différents mécanismes sur le comportement électrique des composants CMOS a ainsi pu être évalué, et une amélioration de la prédictibilité des modèles TCAD a été obtenue sur les dispositifs transistors MOS ainsi que les capteurs d'image CMOS FSI (Front Side Illumination) et BSI (Back Side Illumination).
37
Li, Xin. "Abstractive Representation Modeling for Image Classification." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623250959448677.
Full text
38
Cheng, Hsiang-Fen, and 鄭翔芬. "Image Clustering and Retrieval." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/59657803465700576860.
Full textAbstract:
碩士國立臺灣科技大學
資訊管理系
97
Nowadays, due to the rapid growth of World Wide Web (WWW), a large amount of multimedia data is generated on Internet, which is usually compressed in JPEG format in order to transmit and store efficiently. However, current approaches for content based image retrieval almost focus on uncompressed images. They need to decode images to spatial domain first, which would consume a lot of computation and search time. Therefore, in order to shorten the retrieval time, directly processing the feature extraction and image retrieval from compressed domain can save a lot of time. In addition, the value obtaining by only partial decoding could also represent the image’s characteristic explicitly. Nevertheless, most of approaches in this compressed domain still select a lot of coefficients to represent the image’s features or process those coefficients in additional steps for obtaining image features. However, in this way, the search time will increase dramatically with the size of the image database. Hence, the purpose of this thesis is to extract only a few representative features from the compressed domain, and effectively use these features in image retrieval system such that the images requested by users can be retrieved efficiently. This thesis proposes an efficient image clustering and retrieval approaches. They can improve search time and effectively retrieve the similar images. Using bisecting K-means algorithm, the images from an compressed image database are separated according to the image’s content first, so the retrieval approach is not necessary to search all images in the image database in later processes. Moreover, DC (Direct Current) coefficients are directly extracted from DCT (Discrete Cosine Transformation) domain without fully decoding the compressed images. Therefore, the time of similarity measurement is decreased, and the features extracted from the image database are easy to be managed. In addition, using DC features on the clustering stage and similarity computing stage, the proposed approach can efficiently retrieve the images which match the user’s demand. Experimental results reveal that the proposed approach has highly efficient response time and improves the performance of image retrieval result.
39
Huang, Xiao-Juan, and 黃小娟. "Decision-Tree Based Image Clustering." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/42912242158073405104.
Full textAbstract:
碩士南華大學
資訊管理學系碩士班
90
In this thesis, we propose an image clustering method based on CLTree for image segmentation. CLTree is a clustering algorithm that uses decision-tree technique. It’s quit different from existing clustering methods, and it finds clusters without making any prior assumptions or any input parameters. Whether a clustering is good or bad depends on the user's subjective judgment, so we offer three image segmentation results. The experimental results reveal that all of them perform well.
40
Chen, Ying-Tsung, and 陳瑩聰. "Range Image Segmentation Using Clustering Techniques." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/99390774673000536724.
Full textAbstract:
碩士國立中興大學
機械工程學系所
100
In computer vision, get a useful information directly from the one containing the image of the three-dimensional information has been difficult, the range image segmentation is a very important initial at the object surface in the original image is dividedbasic geometric elements and geometric elements with plane, spherical, cylindrical and conical surface. A mechanical components often contain many different geometric components and if the direct analysis to the image all the pixels likely to cause long computation time, so in this paper,we use of the hierarchical clustering , the first image is divided into small blocksby block to find the components. In this paper,the first step use sign the the Gaussian curvature and mean curvature of the surface morphology,than we use RANSAC algorithm to identify all the surface elements from the sign image.
41
Lee, Ee-Lin, and 李怡霖. "Image enhancement Using the Clustering Filter." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/39396769469611472132.
Full textAbstract:
碩士國立交通大學
電信研究所
83
In this thesis, we develop an efficient detail-preserving filter for image enhancement. It is modified from the clustering filter by Wong which is used as a preprocessing filter for image compr- ession. Basically, the clustering filter is an averaging filter with an exponentially weighting window. Weights inside the window can be adaptively adjusted, such that homogeneous regions are highly smoothed and edges are sharply preserved. Two modifi- cations have been made: First, we propose a different weights- adjusting strategy. Second, we devise a criterion to find the pixels corrupted by impulse. Simulation results show that the proposed filter can achieve a high smoothing efficiency and at the same time well preserves edges and details.
42
Chiu, Chao-Wei, and 邱兆偉. "GPU-Accelerated K-Means Image Clustering." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/02981949068777197122.
Full textAbstract:
碩士國立中興大學
土木工程學系所
102
K-Means clustering has been a widely used approach in unsupervised classification of remotely sensed images. Due to recent emerging development in Graphics Processing Units (GPUs), the computing performance and memory bandwidth of GPUs have been much higher than those of Central Processing Units (CPUs). Therefore, it is expected to accelerate K-Means clustering by parallel computing in GPUs. This research aims on developing a GPU-optimized parallel processing approach for fast unsupervised classification of remotely sensed images using C++ and NVIDIA’s CUDA. The basic idea of traditional K-Means approach was refined with minimum distance classifier in this research for clustering images. The performance of numerical experiments in clustering 3-band color aerial images, in the size of 1360×1020 and scale-down 680×510, into specified number of spectral clusters will be demonstrated for the advantages of 10 to 20 speed-up ratio in computational efficiency of the GPU-based approach in a highly parallel, multi-thread, and multi-core implementation against traditional CPU-based approach.
43
Wang, Jan-Jow, and 王展昭. "Color Image Clustering using Chromatic Feature." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/42277400012116452584.
Full textAbstract:
碩士中華大學
資訊工程學系碩士班
90
By the progress of computer applications, computer peripherals such as color printer, color digital camera, color monitor are often used to generate color image. These peripherals are often used to process the whole color image, but sometimes we just want to process particular colors. Color image clustering can cluster similar colors so that we can do further processing. In this paper, we proposed a color image clustering technique based on chromatic feature analysis. The goal of our study is to find the best color clusters of an image. We extend the idea of histogram. In the past, we usually segment image by histogram thresholding method or color space clustering method, but it’s not enough for color image processing now. First, a peak-finding algorithm is used to obtain initial clusters. Second, the ideas of between class and within class are used to label pixels into class. Finally, in order to reduce the number of clusters, we use standard dev. and average distance as a criterion to archive this job. After a lot of study of color difference and color distance, we choose the CIE Lab system to measure the color difference. We employ the proposed method on many color images. From the results of our experiments, the images applied by our proposed method have better clustering results. We also applied the same color images by traditional histogram method. While comparing and analyzing these results, our approach offers more complex but better color image clustering results.
44
Hung, Ta-Chun, and 洪大鈞. "Image Clustering Using Community Detection Algorithm on Image Similarity Network." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/70333908901559541765.
Full textAbstract:
碩士國立交通大學
資訊學院資訊學程
102
In recent years, with the prevalence and popularity of community networks and mobile devices, digital image produced quite fast. The frequency of the people photographed is more frequent than before. According to statistics from social networks, Facebook、 Instagram and Flickr, they have hundreds of millions of photos uploaded per day. These pictures with a lot of hidden information can represent the characteristics of the users. To identify the characteristics of the users can develop more applications in the community networks, such as: dating system、 community recommendations、 advertising and marketing and so on. In the thesis, we use pictures clustering method to find hidden information of the picture. The pre-processing of image clustering method is to extract image feature, we extract image feature by SIFT (Scale-invariant feature transform) and CLD (Color layout descriptor), and calculate the similarity between images. This experiment uses two clustering method: APC (Affinity propagation clustering) and HIC (Hierarchical image clustering). HIC is a clustering method which this paper propose. HIC have hub node characteristics, the node will also be assigned to more than one community, the representatives of the node and other communities have a high degree of similarity, it will be assigned to multiple groups within. APC can only be assigned to the nodes in the most similar group. Social networks have hundreds of millions of photos uploaded per day. If you want the system can be applied on social networks, the execution time of the system must be very fast 、 produce more number of grouping a large number of the picture and the accuracy is higher. After comparing the experimental results, HIC’s execution time、 measure of clustering number and F1 score are superior to APCs’. HIC which this paper propose is most suitable for the community network applications.
45
LI, JYUN-YU, and 李俊諭. "Parallel Big Image Data Retrieval by Conceptualized Clustering and Un-Conceptualized Clustering." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/hrr3hk.
Full textAbstract:
碩士正修科技大學
資訊管理研究所
107
Content-based image retrieval is a hot topic which has been studied for few decades.Although there have been a number of recent studies proposed on this topic, it is still hard to achieve a high retrieval performance for big image data. To aim at this issue, in this paper,we propose a parallel content-based image retrieval method that efficiently retrieves the relevant images by un-conceptualised clustering and conceptualised clustering. For unconceptualised clustering, the un-conceptualised image data is automatically divided into a number of sets, while the conceptualised image data is divided into multiple sets by conceptualised clustering. Based on the clustering index, the depth-first-search strategy is performed to retrieve the relevant images by parallel comparisons. Through experimental evaluations on a large image dataset, the proposed approach is shown to improve the performance of content-based image retrieval substantially in terms of efficiency.
46
Hsieh, Hsiang-chi, and 謝享奇. "Fuzzy Clustering for Digital Image Recognition Application." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/51790660471343461936.
Full textAbstract:
碩士國立臺灣科技大學
電機工程系
88
The purpose of this thesis is to use fuzzy clustering analytic theory to recognize the characters of the chip. In image processing, we used binary image to determine the chip’s position and applied thinning algorithm to segment characters. When the chip is lopsided, we then used fuzzy-edge operator approach to detect the rim of the chip and to rotate the image by trigonometric formula. A simulated system was created in this thesis and that not only determined the critical value of the fuzzy-edge operator, the range of standard error but also determined the initial value. Finally, we applied all of values and approaches built the system in order to have the best efficiency to recognize the chip.
47
Cheng, Nai-Jui, and 鄭乃瑞. "Color Image Segmentation Using Analytical Clustering Techniques." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/26196362357148737205.
Full textAbstract:
碩士國立交通大學
資訊科學研究所
81
In this thesis, a fast segmentation algorithm for color images based on analytical clustering techniques is presented. A two-class analytical clustering technique which clusters a multi-dimensional input data set into two classes by preserving some invariant features is proposed first.The technique is then applied repeatedly to handle multi-class case using split-merge concept. In the splitting phase, the quantized colors of the input image are clustered and the number of cluster is detected roughly. In the merging phase, two kinds of similarity measures are used to decide whether the merging should be taken or not. When clusters have been formed, cluster centroids are then used as prototypes. Each color pixel is then classified according to its color difference to the generated prototypes. Region connectivity is also used in classifying pixels. Attempts have also been made to compare the performance of the proposed algorithm with other existing algorithms. Experimental results indicate that the proposed algorithm with is fast and it segments images well in many color coordinate systems.
48
Chuang, Eng-Liang, and 莊英良. "A Study of Unsupervised Fingerprint Image Clustering." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/b2ge53.
Full textAbstract:
碩士國立臺北科技大學
通訊與資訊產業研發碩士專班
96
The applications of fingerprint identification become more and more important today. Fingerprint identification not only applies to information security but also contributes to criminal investigation, such as suspect matching . However the fingerprint database is getting larger, it is necessary to manage and increase the speed of matching. In this dissertation, we discuss the method of automatic clustering fingerprints without clustering by person. We cluster the fingerprints which is unknown and make all fingerprints in a cluster that belongs to the same person. If we want to check or mark a fingerprint belonged to whom, we only need to check it’s group by group instead of checking it one by one. So it can save lots of manpower and time cost. In the content, it will present to utilize structural matching and hierarchical agglomerative clustering algorithms to build clusters of fingerprints which base on similarity so as to support a great quantity of fingerprint and the method would be beneficial to search high similarity fingerprint quickly, and furthermore, the cluster of combination can improve the speed of fingerprint matching. In the dissertation , image processing will be applied by several methods, such as : histogram equalization, binarization, normalization, thinning ,etc. Input the fingerprint image and proceed with a large quantity of image enhancement so that obtain the related features. Then use these related features, for example: termination, bifurcation, to compose a feature structure separately. Finally, put the feature structure of two fingerprints into a minutia matching to get the degree of similarity. Base on hierarchical agglomerative clustering algorithm, the fingerprint can be clustered according to the similarity. Moreover, cluster purity and rand index can be provided to be a norm of the quality of cluster.
49
Hsieh, Yu-Heng, and 謝宇恆. "A Novel Clustering Approach for the Image Segmentations." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/fvqvsc.
Full textAbstract:
碩士國立臺北科技大學
自動化科技研究所
101
In this thesis, we propose two novel clustering approaches for the medical image and image color segmentation respectively. First algorithm combines the advantages of K-means and Density-based Spatial Clustering of Applications with Noise (DBSCAN). It can classify the pathological cell and the normal cell to two cluster memberships and the disturbances can also be eliminated from the image. In addition, by image processing process, the pathological cell image can be segmented accurately from the image with pathological cell and normal cell. Finally, some experiments are illustrated to demonstrate that the proposed method is superior to K-means and DBSCAN. In the second algorithm, Fuzzy C means clustering method is utilized to find the cluster of high density areas. Besides, the high density center points are utilized to expand the clusters by DBSCAN method. Finally, some examples are illustrated to demonstrate that the proposed algorithm can accurately classify the object and the color from the image.
50
Hsiang-Yen, Lin, and 林相延. "Color-Based K-means Clustering for Image Segmentation." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/7sh6mq.
Full text