Journal articles: 'Image Clustering'

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Miklautz, Lukas, Dominik Mautz, Muzaffer Can Altinigneli, Christian Böhm, and Claudia Plant. "Deep Embedded Non-Redundant Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5174–81. http://dx.doi.org/10.1609/aaai.v34i04.5961.

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Complex data types like images can be clustered in multiple valid ways. Non-redundant clustering aims at extracting those meaningful groupings by discouraging redundancy between clusterings. Unfortunately, clustering images in pixel space directly has been shown to work unsatisfactory. This has increased interest in combining the high representational power of deep learning with clustering, termed deep clustering. Algorithms of this type combine the non-linear embedding of an autoencoder with a clustering objective and optimize both simultaneously. None of these algorithms try to find multiple non-redundant clusterings. In this paper, we propose the novel Embedded Non-Redundant Clustering algorithm (ENRC). It is the first algorithm that combines neural-network-based representation learning with non-redundant clustering. ENRC can find multiple highly non-redundant clusterings of different dimensionalities within a data set. This is achieved by (softly) assigning each dimension of the embedded space to the different clusterings. For instance, in image data sets it can group the objects by color, material and shape, without the need for explicit feature engineering. We show the viability of ENRC in extensive experiments and empirically demonstrate the advantage of combining non-linear representation learning with non-redundant clustering.

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Soleh, Muhamad, Aniati Murni Arymurthy, and Sesa Wiguna. "CHANGE DETECTION IN MULTI-TEMPORAL IMAGES USING MULTISTAGE CLUSTERING FOR DISASTER RECOVERY PLANNING." Jurnal Ilmu Komputer dan Informasi 11, no. 2 (June 29, 2018): 110. http://dx.doi.org/10.21609/jiki.v11i2.623.

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Change detection analysis on multi-temporal images using various methods have been developed by many researchers in the field of spatial data analysis and image processing. Change detection analysis has many benefit for real world applications such as medical image analysis, valuable material detector, satellite image analysis, disaster recovery planning, and many others. Indonesia is one of the most country that encounter natural disaster. The most memorable disaster was happened in December 26, 2004. Change detection is one of the important part management planning for natural disaster recovery. This article present the fast and accurate result of change detection on multi-temporal images using multistage clustering. There are three main step for change detection in this article, the first step is to find the image difference of two multi-temporal images between the time before disaster and after disaster using operation log ratio between those images. The second step is clustering the difference image using Fuzzy C means divided into three classes. Change, unchanged, and intermediate change region. Afterword the last step is cluster the change map from fuzzy C means clustering using k means clustering, divided into two classes. Change and unchanged region. Both clustering’s based on Euclidian distance.

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Tang, Jun. "Image Registration Using Clustering Algorithm." Advanced Materials Research 108-111 (May 2010): 63–68. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.63.

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This paper proposed a new method of image registration based on clustering algorithm. It used clustering algorithm to cluster all the feature vectors of images, and adopted EM algorithm to optimize the parameters and algorithm. Experimental result shows that the proposed image registration method can improve the precise of image registration, and reduce error.

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Zhu, Wencheng, Jiwen Lu, and Jie Zhou. "Nonlinear subspace clustering for image clustering." Pattern Recognition Letters 107 (May 2018): 131–36. http://dx.doi.org/10.1016/j.patrec.2017.08.023.

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Li, Zhihui, Lina Yao, Sen Wang, Salil Kanhere, Xue Li, and Huaxiang Zhang. "Adaptive Two-Dimensional Embedded Image Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4796–803. http://dx.doi.org/10.1609/aaai.v34i04.5914.

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With the rapid development of mobile devices, people are generating huge volumes of images data every day for sharing on social media, which draws much research attention to understanding the contents of images. Image clustering plays an important role in image understanding systems. Often, most of the existing image clustering algorithms flatten digital images that are originally represented by matrices into 1D vectors as the image representation for the subsequent learning. The drawbacks of vector-based algorithms include limited consideration of spatial relationship between pixels and computational complexity, both of which blame to the simple vectorized representation. To overcome the drawbacks, we propose a novel image clustering framework that can work directly on matrices of images instead of flattened vectors. Specifically, the proposed algorithm simultaneously learn the clustering results and preserve the original correlation information within the image matrix. To solve the challenging objective function, we propose a fast iterative solution. Extensive experiments have been conducted on various benchmark datasets. The experimental results confirm the superiority of the proposed algorithm.

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García Villalba, Luis Javier, Ana Lucila Sandoval Orozco, and Jocelin Rosales Corripio. "Smartphone image clustering." Expert Systems with Applications 42, no. 4 (March 2015): 1927–40. http://dx.doi.org/10.1016/j.eswa.2014.10.018.

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Li, Xutong, Taoying Li, and Yan Wang. "GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement." Algorithms 14, no. 12 (November 29, 2021): 349. http://dx.doi.org/10.3390/a14120349.

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Traditional time-series clustering methods usually perform poorly on high-dimensional data. However, image clustering using deep learning methods can complete image annotation and searches in large image databases well. Therefore, this study aimed to propose a deep clustering model named GW_DC to convert one-dimensional time-series into two-dimensional images and improve cluster performance for algorithm users. The proposed GW_DC consisted of three processing stages: the image conversion stage, image enhancement stage, and image clustering stage. In the image conversion stage, the time series were converted into four kinds of two-dimensional images by different algorithms, including grayscale images, recurrence plot images, Markov transition field images, and Gramian Angular Difference Field images; this last one was considered to be the best by comparison. In the image enhancement stage, the signal components of two-dimensional images were extracted and processed by wavelet transform to denoise and enhance texture features. Meanwhile, a deep clustering network, combining convolutional neural networks with K-Means, was designed for well-learning characteristics and clustering according to the aforementioned enhanced images. Finally, six UCR datasets were adopted to assess the performance of models. The results showed that the proposed GW_DC model provided better results.

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Prades, José, Gonzalo Safont, Addisson Salazar, and Luis Vergara. "Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering." Remote Sensing 12, no. 21 (November 1, 2020): 3585. http://dx.doi.org/10.3390/rs12213585.

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Many tasks in hyperspectral imaging, such as spectral unmixing and sub-pixel matching, require knowing how many substances or materials are present in the scene captured by a hyperspectral image. In this paper, we present an algorithm that estimates the number of materials in the scene using agglomerative clustering. The algorithm is based on the assumption that a valid clustering of the image has one cluster for each different material. After reducing the dimensionality of the hyperspectral image, the proposed method obtains an initial clustering using K-means. In this stage, cluster densities are estimated using Independent Component Analysis. Based on the K-means result, a model-based agglomerative clustering is performed, which provides a hierarchy of clusterings. Finally, a validation algorithm selects a clustering of the hierarchy; the number of clusters it contains is the estimated number of materials. Besides estimating the number of endmembers, the proposed method can approximately obtain the endmember (or spectrum) of each material by computing the centroid of its corresponding cluster. We have tested the proposed method using several hyperspectral images. The results show that the proposed method obtains approximately the number of materials that these images contain.

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Tongbram, Simon. "Clustering-based Image Segmentation Techniques: A Review." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 701–7. http://dx.doi.org/10.5373/jardcs/v12sp7/20202160.

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Mohammed, Shatha J. "Brain Image Segmentation Based on Fuzzy Clustering." Al-Mustansiriyah Journal of Science 28, no. 3 (July 3, 2018): 220. http://dx.doi.org/10.23851/mjs.v28i3.553.

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The segmentation performance is topic to suitable initialization and best configuration of supervisory parameters. In medical image segmentation, the segmentation is very important when the diagnosing becomes very hard in medical images which are not properly illuminated. This paper proposes segmentation of brain tumour image of MRI images based on spatial fuzzy clustering and level set algorithm. After performance evaluation of the proposed algorithm was carried on brain tumour images, the results showed confirm its effectiveness for medical image segmentation, where the brain tumour is detected properly.

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Gao, Song, Chengcui Zhang, and Wei-Bang Chen. "Color Image Segmentation." International Journal of Multimedia Data Engineering and Management 3, no. 3 (July 2012): 66–82. http://dx.doi.org/10.4018/jmdem.2012070104.

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An intuitive way of color image segmentation is through clustering in which each pixel in an image is treated as a data point in the feature space. A feature space is effective if it can provide high distinguishability among objects in images. Typically, in the preprocessing phase, various modalities or feature spaces are considered, such as color, texture, intensity, and spatial information. Feature selection or reduction can also be understood as transforming the original feature space into a more distinguishable space (or subspaces) for distinguishing different content in an image. Most clustering-based image segmentation algorithms work in the full feature space while considering the tradeoff between efficiency and effectiveness. The authors’ observation indicates that often time objects in images can be simply detected by applying clustering algorithms in subspaces. In this paper, they propose an image segmentation framework, named Hill-Climbing based Projective Clustering (HCPC), which utilizes EPCH (an efficient projective clustering technique by histogram construction) as the core framework and Hill-Climbing K-means (HC) for dense region detection, and thereby being able to distinguish image contents within subspaces of a given feature space. Moreover, a new feature space, named HSVrVgVb, is also explored which is derived from Hue, Saturation, and Value (HSV) color space. The scalability of the proposed algorithm is linear to the dimensionality of the feature space, and our segmentation results outperform that of HC and other projective clustering-based algorithms.

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Jin, Yanxia, Xin Zhang, and Yao Jia. "Application of Optimized Partitioning Around Medoid Algorithm in Image Retrieval." International Journal of Distributed Systems and Technologies 12, no. 1 (January 2021): 77–94. http://dx.doi.org/10.4018/ijdst.2021010106.

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In image retrieval, the major challenge is that the number of images in the gallery is large and irregular, which results in low retrieval accuracy. This paper analyzes the disadvantages of the PAM (partitioning around medoid) clustering algorithm in image data classification and the excessive consumption of time in the computation process of searching clustering representative objects using the PAM clustering algorithm. Fireworks particle swarm algorithm is utilized in the optimization process. PF-PAM algorithm, which is an improved PAM algorithm, is proposed and applied in image retrieval. First, extract the feature vector of the image in the gallery for the first clustering. Next, according to the clustering results, the most optimal cluster center is searched through the firework particle swarm algorithm to obtain the final clustering result. Finally, according to the incoming query image, determine the related image category and get similar images. The experimental comparison with other approaches shows that this method can effectively improve retrieval accuracy.

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Anbarasan, Kalaivani, and S. Chitrakala. "Clustering-Based Color Image Segmentation Using Local Maxima." International Journal of Intelligent Information Technologies 14, no. 1 (January 2018): 28–47. http://dx.doi.org/10.4018/ijiit.2018010103.

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Color image segmentation has contributed significantly to image analysis and retrieval of relevant images. Color image segmentation helps the end user subdivide user input images into unique homogenous regions of similar pixels, based on pixel property. The success of image analysis is largely owing to the reliability of segmentation. The automatic segmentation of a color image into accurate regions without over-segmentation is a tedious task. Our paper focuses on segmenting color images automatically into multiple regions accurately, based on the local maxima of the GLCM texture property, with pixels spatially clustered into identical regions. A novel Clustering-based Image Segmentation using Local Maxima (CBIS-LM) method is presented. Our proposed approach generates reliable, accurate and non-overlapping multiple regions for the given user input image. The segmented regions can be automatically annotated with distinct labels which, in turn, help retrieve relevant images based on image semantics.

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Malik, C. K. Mohammed. "Content based Image Retrieval Using Clustering Method." International Academic Journal of Science and Engineering 6, no. 2 (September 26, 2022): 06–12. http://dx.doi.org/10.9756/iajse/v6i2/1910020.

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Content-based image retrieval (CBIR) is the deployment of computer vision methods to the information retrieval challenge, that is, the subject of seeking out digital images in vast databases. Techniques based on automated feature extraction methods for obtaining similar images from image databases are under the purview of CBIR. Traditional content based image retrieval (CBIR) systems extract a single feature at a time and use it to categorize and group images in response to a query. To bridge the gap between high-level concepts and low-level features, our innovative method integrates many feature extraction algorithms. In color-based retrieval, we use quadratic distance formulas to calculate the HSV affinity matrix for photos in the query and the database. Wavelet decomposition at six stages is used in texture-based retrieval. Finding the similarity measures between the query image and the images in the database is done with the help of the Euclidean distance classifier. The integrated method used to decrease the file sizes of the retrieved photographs keeps the user from having to pay as much attention to the process.

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Yang, Senquan, Pu Li, HaoXiang Wen, Yuan Xie, and Zhaoshui He. "K-Hyperline Clustering-Based Color Image Segmentation Robust to Illumination Changes." Symmetry 10, no. 11 (November 7, 2018): 610. http://dx.doi.org/10.3390/sym10110610.

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Color image segmentation is very important in the field of image processing as it is commonly used for image semantic recognition, image searching, video surveillance or other applications. Although clustering algorithms have been successfully applied for image segmentation, conventional clustering algorithms such as K-means clustering algorithms are not sufficiently robust to illumination changes, which is common in real-world environments. Motivated by the observation that the RGB value distributions of the same color under different illuminations are located in an identical hyperline, we formulate color classification as a hyperline clustering problem. We then propose a K-hyperline clustering algorithm-based color image segmentation approach. Experiments on both synthetic and real images demonstrate the outstanding performance and robustness of the proposed algorithm as compared to existing clustering algorithms.

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Dong, Y., D. Fan, Q. Ma, S. Ji, and R. Lei. "EDGE-BASED LOCALLY AGGREGATED DESCRIPTORS FOR IMAGE CLUSTERING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 303–8. http://dx.doi.org/10.5194/isprs-archives-xlii-3-303-2018.

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The current global image descriptors are mostly obtained by using the local image features aggregation, which fail to take full account of the details of the image, resulting in the loss of the semantic content information. It cannot be well used to make a good distinction between the high similarity images. In this paper, a new method of image representation, which can express the whole semantics and detail features of the image, is proposed by combining the edge features of the image. It is used to make a global description of the images and then clustering. The experimental results show that the proposed method is capable of clustering of the similarity images with high accuracy and low error rate.

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Alsaidi, B. K., B. J. Al-Khafaji, and S. A. A. Wahab. "Content Based Image Clustering Technique Using Statistical Features and Genetic Algorithm." Engineering, Technology & Applied Science Research 9, no. 2 (April 10, 2019): 3892–95. http://dx.doi.org/10.48084/etasr.2497.

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Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering. The extraction of features gave a high distinguishability and helped GA reach the solution more accurately and faster.

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Kovalenko, A. S., and Y. M. Demyanenko. "Image clustering by autoencoders." Information Technology and Nanotechnology, no. 2391 (2019): 243–49. http://dx.doi.org/10.18287/1613-0073-2019-2391-243-249.

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This paper describes an approach to solving the problem of finding similar images by visual similarity using neural networks on previously unmarked data. We propose to build special architecture of the neural network - autoencoder, through which high-level features are extracted from images. The search for the nearest elements is realized by the Euclidean metric in the generated feature space, after a preliminary decomposition into two-dimensional space. Proposed approach of generate feature space can be applied to the classification task using pre-clustering.

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David S, Alex, Almas Begum, and Ravikumar S. "Content clustering for MRI Image compression using PPAM." International Journal of Engineering & Technology 7, no. 1.7 (February 5, 2018): 126. http://dx.doi.org/10.14419/ijet.v7i1.7.10631.

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Image compression helps to save the utilization of memory, data while transferring the images between nodes. Compression is one of the key technique in medical image. Both lossy and lossless compressions where used based on the application. In case of medical imaging each and every components of pixel is very important hence its nature to chose lossless compression medical images. MRI images are compressed after processing. Here in this paper we have used PPMA method to compress the MRI image. For retrieval of the compressed image content clustering method used.

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Zhou, Yu, Wei Guo Zhang, and Li Feng Li. "A Variational Level Set Model Based on Local Clustering for Image Segmentation." Applied Mechanics and Materials 556-562 (May 2014): 4797–801. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4797.

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For images with intensity inhomogeneities that can’t get accurate segmentation results, this paper proposes a variational level set model based on local clustering. First,based on the model of images with intensity inhomogeneities, we use the K-mean clustering algorithm for intensity clustering in a neighborhood of each point of images with intensity inhomogeneities, and define a local clustering criterion function for the image intensities in the neighborhood. Then this local clustering criterion function is then integrated with respect to the neighborhood center to give a global criterion of image segmentation. This criterion defines an energy function as a local intensity fitting term in the level set model. By minimizing this energy, our method is able to get the accurate image segmentation. The image segmentation results prove that our model in the aspect of segmenting images with intensity inhomogeneity is better than piecewise constant (PC) models, and the segmentation efficiency is higher than region-scalable fitting (RSF) model.

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Yaguchi, Yuichi, and Ryuichi Oka. "Spherical Visualization of Image Data with Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 4 (July 20, 2013): 573–80. http://dx.doi.org/10.20965/jaciii.2013.p0573.

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This paper proposes to aid the search for images by visualization of the image data on a spherical surface. Many photographs were lost in the Tohoku tsunami, and those that were eventually found are now being scanned. However, the owners of the lost photographs are finding it difficult to search for their images within a large set of scanned images that contain no additional information. In this paper, we apply a spatial clustering technique called the Associated Keyword Space (ASKS) projected from a threedimensional (3D) sphere to a two-dimensional (2D) spherical surface for 2D visualization. ASKS supports clustering, and therefore, we construct an image search system in which similar images are clustered. In this system, similar images are identified by color inspection and by having similar characteristics. In this way, the system is able to support the search for images from within a huge number of images.

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Karthikeyan, M., and P. Aruna. "Probability based document clustering and image clustering using content-based image retrieval." Applied Soft Computing 13, no. 2 (February 2013): 959–66. http://dx.doi.org/10.1016/j.asoc.2012.09.013.

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Tuan, Tran Manh, Phung The Huan, Pham Huy Thong, Tran Thi Ngan, and Le Hoang Son. "AN IMPROVEMENT OF TRUSTED SAFE SEMI-SUPERVISED FUZZY CLUSTERING METHOD WITH MULTIPLE FUZZIFIERS." Journal of Computer Science and Cybernetics 38, no. 1 (March 20, 2022): 47–61. http://dx.doi.org/10.15625/1813-9663/38/1/16720.

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Data clustering are applied in various fields such as document classification, dental X-ray image segmentation, medical image segmentation, etc. Especially, clustering algorithms are used in satellite image processing in many important application areas, including classification of vehicles participating in traffic, logistics, classification of satellite images to forecast droughts, floods, forest fire, etc. In the process of collecting satellite image data, there are a number of factors such as clouds, weather, ... that can affect to image quality. Images with low quality will make the performance of clustering algorithms decrease. Apart from that, the parameter of fuzzification in clustering algorithms also affects to clustering results. In the past, clustering methods often used the same fuzzification parameter, m = 2. But in practice, each element should have its own parameter m. Therefore, determining the parameters m is necessary to increase fuzzy clustering performance. In this research, an improvement algorithm for the data partition with confidence problem and multi fuzzifier named as TS3MFCM is introduced. The proposed method consists of three steps namely as “FCM for labeled data”, “Data transformation”, and “Semi-supervised fuzzy clustering with multiple point fuzzifiers”. The proposed TS3MFCM method is implemented and experimentally compared against with the Confidence-weighted Safe Semi-Supervised Clustering (CS3FCM). The performance of proposed method is better than selected methods in both computational time and clustering accuracy on the same datasets

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AHMED, Nasir, and Abdul JALIL. "Multimode Image Clustering Using Optimal Image Descriptor." IEICE Transactions on Information and Systems E97.D, no. 4 (2014): 743–51. http://dx.doi.org/10.1587/transinf.e97.d.743.

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Zalesky, B. A. "Multilevel algorithm for color clustering of images." Doklady of the National Academy of Sciences of Belarus 65, no. 3 (July 16, 2021): 269–74. http://dx.doi.org/10.29235/1561-8323-2021-65-3-269-274.

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The fast multilevel algorithm to cluster color images (MACC – Multilevel Algorithm for Color Clustering) is presented. Currently, several well-known algorithms of image clustering, including the k‑means algorithm (which is one of the most commonly used in data mining) and its fuzzy versions, watershed, region growing ones, as well as a number of new more complex neural network and other algorithms are actively used for image processing. However, they cannot be applied for clustering large color images in real time. Fast clustering is required, for example, to process frames of video streams shot by various video cameras or when working with large image databases. The developed algorithm MACC allows the clustering of large images, for example, FullHD size, on a personal computer with an average deviation from the original color values of about five units in less than 20 milliseconds, while a parallel version of the classical k‑means algorithm performs the clustering of the same images with an average error of more than 12 units for a time exceeding 2 seconds. The proposed algorithm of multilevel color clustering of images is quite simple to implement. It has been extensively tested on a large number of color images.

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OMRAN, M., A. P. ENGELBRECHT, and A. SALMAN. "PARTICLE SWARM OPTIMIZATION METHOD FOR IMAGE CLUSTERING." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 03 (May 2005): 297–321. http://dx.doi.org/10.1142/s0218001405004083.

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An image clustering method that is based on the particle swarm optimizer (PSO) is developed in this paper. The algorithm finds the centroids of a user specified number of clusters, where each cluster groups together with similar image primitives. To illustrate its wide applicability, the proposed image classifier has been applied to synthetic, MRI and satellite images. Experimental results show that the PSO image classifier performs better than state-of-the-art image classifiers (namely, K-means, Fuzzy C-means, K-Harmonic means and Genetic Algorithms) in all measured criteria. The influence of different values of PSO control parameters on performance is also illustrated.

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Feng, Guanyuan, Zhengang Jiang, Xuezhi Tan, and Feihao Cheng. "Hierarchical Clustering-Based Image Retrieval for Indoor Visual Localization." Electronics 11, no. 21 (November 4, 2022): 3609. http://dx.doi.org/10.3390/electronics11213609.

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Visual localization is employed for indoor navigation and embedded in various applications, such as augmented reality and mixed reality. Image retrieval and geometrical measurement are the primary steps in visual localization, and the key to improving localization efficiency is to reduce the time consumption of the image retrieval. Therefore, a hierarchical clustering-based image-retrieval method is proposed to hierarchically organize an off-line image database, resulting in control of the time consumption of image retrieval within a reasonable range. The image database is hierarchically organized by two stages: scene-level clustering and sub-scene-level clustering. In scene-level clustering, an improved cumulative sum algorithm is proposed to detect change points and then group images by global features. On the basis of scene-level clustering, a feature tracking-based method is introduced to further group images into sub-scene-level clusters. An image retrieval algorithm with a backtracking mechanism is designed and applied for visual localization. In addition, a weighted KNN-based visual localization method is presented, and the estimated query position is solved by the Armijo–Goldstein algorithm. Experimental results indicate that the running time of image retrieval does not linearly increase with the size of image databases, which is beneficial to improving localization efficiency.

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Farmaha, Ihor, Marian Banaś, Vasyl Savchyn, Bohdan Lukashchuk, and Taras Farmaha. "Wound image segmentation using clustering based algorithms." New Trends in Production Engineering 2, no. 1 (October 1, 2019): 570–78. http://dx.doi.org/10.2478/ntpe-2019-0062.

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Abstract Classic methods of measurement and analysis of the wounds on the images are very time consuming and inaccurate. Automation of this process will improve measurement accuracy and speed up the process. Research is aimed to create an algorithm based on machine learning for automated segmentation based on clustering algorithms Methods. Algorithms used: SLIC (Simple Linear Iterative Clustering), Deep Embedded Clustering (that is based on artificial neural networks and k-means). Because of insufficient amount of labeled data, classification with artificial neural networks can't reach good results. Clustering, on the other hand is an unsupervised learning technique and doesn't need human interaction. Combination of traditional clustering methods for image segmentation with artificial neural networks leads to combination of advantages of both of them. Preliminary step to adapt Deep Embedded Clustering to work with bio-medical images is introduced and is based on SLIC algorithm for image segmentation. Segmentation with this method, after model training, leads to better results than with traditional SLIC.

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Liu, Hongbing, Fan Zhang, Chang-an Wu, and Jun Huang. "Image Superresolution Reconstruction via Granular Computing Clustering." Computational Intelligence and Neuroscience 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/219636.

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The problem of generating a superresolution (SR) image from a single low-resolution (LR) input image is addressed via granular computing clustering in the paper. Firstly, and the training images are regarded as SR image and partitioned into some SR patches, which are resized into LS patches, the training set is composed of the SR patches and the corresponding LR patches. Secondly, the granular computing (GrC) clustering is proposed by the hypersphere representation of granule and the fuzzy inclusion measure compounded by the operation between two granules. Thirdly, the granule set (GS) including hypersphere granules with different granularities is induced by GrC and used to form the relation between the LR image and the SR image by lasso. Experimental results showed that GrC achieved the least root mean square errors between the reconstructed SR image and the original image compared with bicubic interpolation, sparse representation, and NNLasso.

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Zhang, Chengcui, Liping Zhou, Wen Wan, Jeffrey Birch, and Wei-Bang Chen. "An Image Clustering and Feedback-based Retrieval Framework." International Journal of Multimedia Data Engineering and Management 1, no. 1 (January 2010): 55–74. http://dx.doi.org/10.4018/jmdem.2010111204.

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Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region (object) can hardly represent the user’s retrieval target, especially when more than one object of interest is involved in the retrieval. Integrated Region Matching (IRM) has been used to improve the retrieval accuracy by evaluating the overall similarity between images and incorporating the properties of all the regions in the images. However, IRM does not take the user’s preferred regions into account and has undesirable time complexity. In this article, we present a Feedback-based Image Clustering and Retrieval Framework (FIRM) using a novel image clustering algorithm and integrating it with Integrated Region Matching (IRM) and Relevance Feedback (RF). The performance of the system is evaluated on a large image database, demonstrating the effectiveness of our framework in catching users’ retrieval interests in object-based image retrieval.

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Manoharan, Dr Samuel. "Performance Analysis of Clustering Based Image Segmentation Techniques." Journal of Innovative Image Processing 2, no. 1 (March 11, 2020): 14–24. http://dx.doi.org/10.36548/jiip.2020.1.002.

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As the images are examined using the latest machine learning process, the techniques for computing the images become highly essential. This computation applied over the images allows one to have an assessable information’s or values from the images. Since segmentation plays a vital role in processing of images by enhancing or hypothetically altering the images making the examination of valuable insights easier. Several procedures and the methods for segmenting the images have been developed. However to have an better process it is important to sort out an effective segmentation procedure, so the paper performs the analysis of the clustering based image segmentation techniques applied on the magnetic resonance image of the human brain to detect the white matter hyper intensities part. The evaluation process take place in the MATLAB to evince the accurate valuation procedure. The optimal procedure is sorted out to be used in observing and examining the medical images by implementing over a computer assisted tool.

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Sanmorino, Ahmad. "Clustering Batik Images using Fuzzy C-Means Algorithm Based on Log-Average Luminance." Computer Engineering and Applications Journal 1, no. 1 (June 26, 2012): 25–31. http://dx.doi.org/10.18495/comengapp.v1i1.3.

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Batik is a fabric or clothes that are made â€‹â€‹with a special staining technique called wax-resist dyeing and is one of the cultural heritage which has high artistic value. In order to improve the efficiency and give better semantic to the image, some researchers apply clustering algorithm for managing images before they can be retrieved. Image clustering is a process of grouping images based on their similarity. In this paper we attempt to provide an alternative method of grouping batik image using fuzzy c-means (FCM) algorithm based on log-average luminance of the batik. FCM clustering algorithm is an algorithm that works using fuzzy models that allow all data from all cluster members are formed with different degrees of membership between 0 and 1. Log-average luminance (LAL) is the average value of the lighting in an image. We can compare different image lighting from one image to another using LAL. From the experiments that have been made, it can be concluded that fuzzy c-means algorithm can be used for batik image clustering based on log-average luminance of each image possessed.DOI:Â 10.18495/comengapp.11.025031

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Arasy, Muhammad Hariz, Suyanto Suyanto, and Kurniawan Nur Ramadhani. "Aerial Image Segmentation with Clustering Using Fireworks Algorithm." Indonesian Journal on Computing (Indo-JC) 4, no. 1 (March 22, 2019): 19. http://dx.doi.org/10.21108/indojc.2019.4.1.245.

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Aerial images has different data characteristics when compared to other types of images. An aerial image usually contains small insignificant objects that can cause errors in the unsupervised segmentation method. K-means clustering, one of the widely used unsupervised image segmentation methods, is highly vulnerable to local optima. In this study, Adaptive Fireworks Algorithm (AFWA) is proposed as an alternative to the K-means algorithm in optimizing the clustering process in the cluster-based segmentation method. AFWA is then applied to perform aerial image segmentation and the results are compared with K-means. Based on the comparison using Probabilistic Rand Index (PRI) and Variation of Information (VI) evaluation metrics, AFWA produces an overall better segmentation quality.

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Shirsath, Asmita Bhaskar, M. J. Chouhan, and N. J. Uke. "Image Retrieval Based on WBCH and Clustering Algorithm." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 5, no. 3 (September 15, 2013): 604–13. http://dx.doi.org/10.24297/ijmit.v5i3.761.

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Research on content-based image retrieval has gained tremendous momentum during the last decade. Color, texture and shape information have been the primitive image descriptors in content based image retrieval systems. In order to get faster retrieval result from large-scale image database ,we proposed image retrieval system in which image database is first pre-processed by Wavelet Based Color Histogram (WBCH) and K-means algorithm and then using Hierarchical clustering algorithm we index the previous result and then by using similarity measures we retrieve the images from pre-processed database. Experiments show that this proposed method offers substantial increase in retrieval speed but needs to be improved on retrieval results.

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Jain, Geerisha. "Satellite Image Processing Using Fuzzy Logic and Modified K-Means Clustering Algorithm for Image Segmentation." Computational Intelligence and Machine Learning 3, no. 2 (October 14, 2022): 57–61. http://dx.doi.org/10.36647/ciml/03.02.a008.

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Satellite images are useful in providing a real time dynamic picture of the earth and its environment. The large assemblage of remote sensing satellites orbiting the earth provide an extensive and periodic coverage of the planet through the capture of live images round the clock, in turn enabling numerous uses for the benefit of mankind. In the field of satellite image processing, image segmentation is one of the vital steps for extracting and gathering huge amount of information from the satellite images. The basic k-means clustering algorithm is simple and fast in terms of dealing with the required segmentation, but the limitation associated with this clustering is its inability to produce the same result for every run, as the resulting clusters depends on the initial random assignments. In this paper, an enhanced modified k-means clustering algorithm is proposed for the effective segmentation of the satellite images with an objective to overcome the demerits of the traditional k-means by combining fuzzy logic with the membership function. The proposed methodology continuously produces the same result for each run. As an outcome, the experimental results proved that the enhanced k-means algorithm is an effective and more efficient process for the precise and accurate segmentation of satellite images. Index Terms : Image Segmentation, Satellite Imagery, Fuzzy logic, K-Means, Clustering.

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Bo, Yu, Peng Shi, Can Wang, Fang Qin, and Huimei Wei. "Image Segmentation Algorithm of Colorimetric Sensor Array Based on Fuzzy C-Means Clustering." Mobile Information Systems 2022 (August 21, 2022): 1–8. http://dx.doi.org/10.1155/2022/8333054.

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In the real world, the boundaries between many objective things are often fuzzy. When classifying things, they are accompanied by ambiguity, which leads to fuzzy cluster analysis. The most typical in fuzzy clustering analysis is the fuzzy C-means clustering algorithm. The fuzzy C-means clustering algorithm obtains the membership degree of each sample point to all the class centers by optimizing the objective function, so as to determine the category of the sample point to achieve the purpose of automatically classifying the sample data. Based on fuzzy C-means clustering, this paper analyzes the image segmentation algorithm of the chroma sensor array. The fuzzy C-means (FCM) algorithm for colorimetric sensor array image segmentation is an unsupervised fuzzy clustering and recalibration process, which is suitable for the existence of blur and uncertainty in colorimetric sensor array images. However, this algorithm has inherent defects; that is, it does not combine the characteristics of the current colorimetric sensor array diversity and instability, does not consider the spatial information of the pixels, and only uses the grayscale information of the image, making it effective for noise. The image segmentation effect is not ideal. Therefore, this paper proposes a new colorimetric sensor array image segmentation algorithm based on fuzzy C-means clustering. Through the image segmentation effect test, the image segmentation algorithm proposed in this paper demonstrates an overall optimal segmentation accuracy of 96.62% in all array point image segmentation, which can effectively and accurately achieve the target extraction of colorimetric sensor array images.

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Tang, Liming. "A Variational Level Set Model Combined with FCMS for Image Clustering Segmentation." Mathematical Problems in Engineering 2014 (2014): 1–24. http://dx.doi.org/10.1155/2014/145780.

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The fuzzy C means clustering algorithm with spatial constraint (FCMS) is effective for image segmentation. However, it lacks essential smoothing constraints to the cluster boundaries and enough robustness to the noise. Samson et al. proposed a variational level set model for image clustering segmentation, which can get the smooth cluster boundaries and closed cluster regions due to the use of level set scheme. However it is very sensitive to the noise since it is actually a hard C means clustering model. In this paper, based on Samson’s work, we propose a new variational level set model combined with FCMS for image clustering segmentation. Compared with FCMS clustering, the proposed model can get smooth cluster boundaries and closed cluster regions due to the use of level set scheme. In addition, a block-based energy is incorporated into the energy functional, which enables the proposed model to be more robust to the noise than FCMS clustering and Samson’s model. Some experiments on the synthetic and real images are performed to assess the performance of the proposed model. Compared with some classical image segmentation models, the proposed model has a better performance for the images contaminated by different noise levels.

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Civicioglu, P., U. H. Atasever, C. Ozkan, E. Besdok, A. E. Karkinli, and A. Kesikoglu. "Performance Comparison Of Evolutionary Algorithms For Image Clustering." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 71–74. http://dx.doi.org/10.5194/isprsarchives-xl-7-71-2014.

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Evolutionary computation tools are able to process real valued numerical sets in order to extract suboptimal solution of designed problem. Data clustering algorithms have been intensively used for image segmentation in remote sensing applications. Despite of wide usage of evolutionary algorithms on data clustering, their clustering performances have been scarcely studied by using clustering validation indexes. In this paper, the recently proposed evolutionary algorithms (i.e., Artificial Bee Colony Algorithm (ABC), Gravitational Search Algorithm (GSA), Cuckoo Search Algorithm (CS), Adaptive Differential Evolution Algorithm (JADE), Differential Search Algorithm (DSA) and Backtracking Search Optimization Algorithm (BSA)) and some classical image clustering techniques (i.e., k-means, fcm, som networks) have been used to cluster images and their performances have been compared by using four clustering validation indexes. Experimental test results exposed that evolutionary algorithms give more reliable cluster-centers than classical clustering techniques, but their convergence time is quite long.

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Xu, Mengxi, and Chenglin Wei. "Remotely Sensed Image Classification by Complex Network Eigenvalue and Connected Degree." Computational and Mathematical Methods in Medicine 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/632703.

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It is a well-known problem of remotely sensed images classification due to its complexity. This paper proposes a remotely sensed image classification method based on weighted complex network clustering using the traditionalK-means clustering algorithm. First, the degree of complex network and clustering coefficient of weighted feature are used to extract the features of the remote sensing image. Then, the integrated features of remote sensing image are combined to be used as the basis of classification. Finally,K-means algorithm is used to classify the remotely sensed images. The advantage of the proposed classification method lies in obtaining better clustering centers. The experimental results show that the proposed method gives an increase of 8% in accuracy compared with the traditionalK-means algorithm and the Iterative Self-Organizing Data Analysis Technique (ISODATA) algorithm.

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Zhao, Feng, Hao Hao, and Hanqiang Liu. "Robust intuitionistic fuzzy clustering with bias field estimation for noisy image segmentation." Intelligent Data Analysis 26, no. 5 (September 5, 2022): 1403–26. http://dx.doi.org/10.3233/ida-216058.

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The concept of intuitionistic fuzzy set has been found to be highly useful to handle vagueness in data. Based on intuitionistic fuzzy set theory, intuitionistic fuzzy clustering algorithms are proposed and play an important role in image segmentation. However, due to the influence of initialization and the presence of noise in the image, intuitionistic fuzzy clustering algorithm cannot acquire the satisfying performance when applied to segment images corrupted by noise. In order to solve above problems, a robust intuitionistic fuzzy clustering with bias field estimation (RIFCB) is proposed for noisy image segmentation in this paper. Firstly, a noise robust intuitionistic fuzzy set is constructed to represent the image by using the neighboring information of pixels. Then, initial cluster centers in RIFCB are adaptively determined by utilizing the frequency statistics of gray level in the image. In addition, in order to offset the information loss of the image when constructing the intuitionistic fuzzy set of the image, a new objective function incorporating a bias field is designed in RIFCB. Based on the new initialization strategy, the intuitionistic fuzzy set representation, and the incorporation of bias field, the proposed method preserves the image details and is insensitive to noise. Experimental results on some Berkeley images show that the proposed method achieves satisfactory segmentation results on images corrupted by different kinds of noise in contrast to conventional fuzzy clustering algorithms.

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Ma, Guo Qiang, and Xiao Juan Wang. "An Efficient Algorithm Optimization of CT Images Segmentation Based on K-Means Clustering." Applied Mechanics and Materials 530-531 (February 2014): 386–89. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.386.

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Computer tomography image (CT Image) segmentation algorithms have a number of advantages. However, most of these image segmentation algorithms suffer from long computation time because the number of pixels and the encoding parameters is large. We optimized the k-means clustering program with MATLAB language in order to improve the efficiency and stability of k-clustering algorithm in CT image segmentation. One hundred CT images are used to test the proposed method code and compare with the k-means function of the MATLAB R2012a Statistics Toolbox. We analyzed the difference of the two kinds program running time using single factor analysis of variance (ANOVA) and observed the efficiency and robustness of the segmentation results. The experimental results show that the optimized k-means clustering algorithm code has higher efficiency and robustness of segmentation. High performance of the proposed k-means clustering program is illustrated in terms of both the evaluation performance and computation time, compared with some current segmentation methods. It is empirically shown that the proposed k-means clustering program is robust and efficient for CT images segmentation.

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Liu, Ruochen, Minlei Xue, and Haoyuan Lv. "Adaptive Feature Weights Based Double-Layer Multi-Objective Method for SAR Image Segmentation." Remote Sensing 14, no. 5 (February 24, 2022): 1117. http://dx.doi.org/10.3390/rs14051117.

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The recently proposed multi-objective clustering methods convert the segmentation problem to a multi-objective optimization problem by extracting multiple features from an image to be segmented as clustering data. However, most of these methods fail to consider the impacts of different features on segmentation results when calculating the similarity using the Euclidean distance. In this paper, feature domination is defined to segment the image efficiently, and then an adaptive feature weights based double-layer multi-objective method (AFWDLMO) for image segmentation is presented. The proposed method mainly contains two layers: a weight determination layer and a clustering layer. In the weight determination layer, AFWDLMO adaptively identifies the dominant feature of an image to be segmented and specifies its optimal weight through differential evolution. In the clustering layer, multi-objective clustering functions are established and optimized based on the acquired optimal weight, and a set of solutions with high segmentation accuracy is found. The segmentation results on several texture images and SAR images show that the proposed method is better than several existing state-of-the-art segmentation algorithms.

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Yu, Miao, and Xiaojie Liu. "Computer Image Content Retrieval considering K-Means Clustering Algorithm." Mathematical Problems in Engineering 2022 (May 21, 2022): 1–7. http://dx.doi.org/10.1155/2022/7914842.

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The traditional computer image content retrieval technology can only meet the specific requirements of customers; because of its general features, it cannot comply with the requirements of various environments, purposes, and time simultaneously. This study presents a computer image content retrieval method for a K-means clustering algorithm (KCA). The information collected by computer is preprocessed by K-means clustering algorithm, and the unacquired computer image is labeled based on the optimal learning order according to the KCA. The K-means clustering algorithm classifies the color, pattern, shape, and content of computer images and takes advantage of the invariance advantages of image content retrieval such as scale, rotation, illumination, and blur correction to effectively solve the recurring problems of computer images during retrieval and increase its accuracy. The results of the experiment indicate that the proposed K-means clustering algorithm can enhance the efficiency and performance effectively during image retrieval by computer as compared to the traditional content search algorithm and also help to quickly converge to the query content; it also shows that KCA has good robustness.

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Malhi, Umar Subhan, Junfeng Zhou, Cairong Yan, Abdur Rasool, Shahbaz Siddeeq, and Ming Du. "Unsupervised Deep Embedded Clustering for High-Dimensional Visual Features of Fashion Images." Applied Sciences 13, no. 5 (February 22, 2023): 2828. http://dx.doi.org/10.3390/app13052828.

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Fashion image clustering is the key to fashion retrieval, forecasting, and recommendation applications. Manual labeling-based clustering is both time-consuming and less accurate. Currently, popular methods for extracting features from data use deep learning techniques, such as a Convolutional Neural Network (CNN). These methods can generate high-dimensional feature vectors, which are effective for image clustering. However, high dimensions can lead to the curse of dimensionality, which makes subsequent clustering difficult. The fashion images-oriented deep clustering method (FIDC) is proposed in this paper. This method uses CNN to generate a 4096-dimensional feature vector for each fashion image through migration learning, then performs dimensionality reduction through a deep-stacked auto-encoder model, and finally performs clustering on these low-dimensional vectors. High-dimensional vectors can represent images, and dimensionality reduction avoids the curse of dimensionality during clustering tasks. A particular point in the method is the joint learning and optimization of the dimensionality reduction process and the clustering task. The optimization process is performed using two algorithms: back-propagation and stochastic gradient descent. The experimental findings show that the proposed method, called FIDC, has achieved state-of-the-art performance.

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Yu, Jinhua, and Yuanyuan Wang. "Molecular Image Segmentation Based on Improved Fuzzy Clustering." International Journal of Biomedical Imaging 2007 (2007): 1–9. http://dx.doi.org/10.1155/2007/25182.

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Segmentation of molecular images is a difficult task due to the low signal-to-noise ratio of images. A novel two-dimensional fuzzy C-means (2DFCM) algorithm is proposed for the molecular image segmentation. The 2DFCM algorithm is composed of three stages. The first stage is the noise suppression by utilizing a method combining a Gaussian noise filter and anisotropic diffusion techniques. The second stage is the texture energy characterization using a Gabor wavelet method. The third stage is introducing spatial constraints provided by the denoising data and the textural information into the two-dimensional fuzzy clustering. The incorporation of intensity and textural information allows the 2DFCM algorithm to produce satisfactory segmentation results for images corrupted by noise (outliers) and intensity variations. The 2DFCM can achieve0.96±0.03segmentation accuracy for synthetic images under different imaging conditions. Experimental results on a real molecular image also show the effectiveness of the proposed algorithm.

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Yu, Siquan, Jiaxin Liu, Zhi Han, Yong Li, Yandong Tang, and Chengdong Wu. "Representation Learning Based on Autoencoder and Deep Adaptive Clustering for Image Clustering." Mathematical Problems in Engineering 2021 (January 9, 2021): 1–11. http://dx.doi.org/10.1155/2021/3742536.

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Image clustering is a complex procedure, which is significantly affected by the choice of image representation. Most of the existing image clustering methods treat representation learning and clustering separately, which usually bring two problems. On the one hand, image representations are difficult to select and the learned representations are not suitable for clustering. On the other hand, they inevitably involve some clustering step, which may bring some error and hurt the clustering results. To tackle these problems, we present a new clustering method that efficiently builds an image representation and precisely discovers cluster assignments. For this purpose, the image clustering task is regarded as a binary pairwise classification problem with local structure preservation. Specifically, we propose here such an approach for image clustering based on a fully convolutional autoencoder and deep adaptive clustering (DAC). To extract the essential representation and maintain the local structure, a fully convolutional autoencoder is applied. To manipulate feature to clustering space and obtain a suitable image representation, the DAC algorithm participates in the training of autoencoder. Our method can learn an image representation that is suitable for clustering and discover the precise clustering label for each image. A series of real-world image clustering experiments verify the effectiveness of the proposed algorithm.

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Kumar, Aayush, Abhishek Kumar, and Kuldeep Kashyap. "Image to Image Search using K-means Clustering." International Journal of Computer Applications 182, no. 46 (March 15, 2019): 18–21. http://dx.doi.org/10.5120/ijca2019918606.

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Zhou, Lei, and Weiyufeng Wei. "DIC: Deep Image Clustering for Unsupervised Image Segmentation." IEEE Access 8 (2020): 34481–91. http://dx.doi.org/10.1109/access.2020.2974496.

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R, Nithyananda C., and Ramachandra A. C. "Adaptive Image Enhancement Using Image Properties and Clustering." International Journal of Image, Graphics and Signal Processing 8, no. 8 (August 8, 2016): 9–14. http://dx.doi.org/10.5815/ijigsp.2016.08.02.

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Cheng, Wenlong, Tommy W. S. Chow, and Mingbo Zhao. "Locality Constrained-ℓ Sparse Subspace Clustering for Image Clustering." Neurocomputing 205 (September 2016): 22–31. http://dx.doi.org/10.1016/j.neucom.2016.04.010.

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Journal articles on the topic 'Image Clustering'

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