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Journal articles on the topic 'Content-based features'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Anuradha, Shitole1 and Uma Godase2. "Survey on Content Based Image Retrieval." International Journal of Computer-Aided Technologies (IJCAx) 01, dec (2014): 01–09. https://doi.org/10.5281/zenodo.1450266.

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Invention of digital technology has lead to increase in the number of images that can be stored in digital format. So searching and retrieving images in large image databases has become more challenging. From the last few years, Content Based Image Retrieval (CBIR) gained increasing attention from researcher. CBIR is a system which uses visual features of image to search user required image from large image database and user’s requests in the form of a query image. Important features of images are colour, texture and shape which give detailed information about the image. CBIR techniques

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Sajwan, Vijaylakshmi. "Content Based Image Retrieval Using Combined Features (Color and Texture)." International Journal of Engineering Research 3, no. 4 (2014): 271–73. http://dx.doi.org/10.17950/ijer/v3s4/421.

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Nair, Prashant, Dhileep Kumar, and Ramesh Shahabadkar. "Content Based Image Retrieval Based on Colour and Texture Features Using Hog Descriptor." Global Journal For Research Analysis 3, no. 8 (2012): 99–101. http://dx.doi.org/10.15373/22778160/august2014/30.

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Giveki, Davar, Mohammad Ali Soltanshahi, and Gholam Ali Montazer. "A new image feature descriptor for content based image retrieval using scale invariant feature transform and local derivative pattern." Optik - International Journal for Light and Electron Optics 131 (June 7, 2016): 242–54. https://doi.org/10.5281/zenodo.13998082.

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This paper presents a new methodology to retrieve images of different scenes by introducing a novel image descriptor.&lrm; The proposed descriptor works with Scale Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Local Derivative Pattern (LDP), Local Ternary Pattern (LTP) and any other feature descriptor that can be applied on the image pixels.&lrm; As the proposed descriptor considers a group of pixels together, higher level of semantic is achieved.&lrm; In this work, a new image descriptor using SIFT and LDP is introduced that is able to

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Banerjee, Minakshi, and Malay K. Kundu. "Edge based features for content based image retrieval." Pattern Recognition 36, no. 11 (2003): 2649–61. http://dx.doi.org/10.1016/s0031-3203(03)00174-2.

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Deng, Shengli, Fan Wang, Haowei Wang, and Yunna Cai. "Recognizing Implicitly Toxic Content Based on Multiple Attention Mechanisms." Proceedings of the Association for Information Science and Technology 61, no. 1 (2024): 880–82. http://dx.doi.org/10.1002/pra2.1127.

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ABSTRACTToxic content on social media has posed a significant threat to user experience and societal stability. In contrast to explicitly toxic content, implicitly toxic content, lacking overt toxic words, is challenging to identify through single‐text features. Therefore, this study proposes a multi‐feature fusion algorithm to recognize implicit toxic content. Firstly, we collect various features for each post, including text content, likes, comments, user information and other features. Subsequently, employing a multi‐head attention mechanism, we extract and fuse these features. Then, utiliz

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Ayan, Mehmet, O. Ayhan Erdem, and Hasan Şakir Bilge. "Multi-Featured Content-Based Image Retrieval Using Color and Texture Features." Applied Mechanics and Materials 850 (August 2016): 136–43. http://dx.doi.org/10.4028/www.scientific.net/amm.850.136.

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Content-based image retrieval (CBIR) system becomes a hot topic in recent years. CBIR system is the retrieval of images based on visual features. CBIR system based on a single feature has a low performance. Therefore, in this paper a new content based image retrieval method using color and texture features is proposed to improve performance. In this method color histogram and color moment are used for color feature extraction and grey level co-occurrence matrix (GLCM) is used for texture feature extraction. Then all extracted features are integrated for image retrieval. Finally, color histogra

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Tena, Silvester, Rudy Hartanto, and Igi Ardiyanto. "Content-based image retrieval for fabric images: A survey." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (2021): 1861. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1861-1872.

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In <span>recent years, a great deal of research has been conducted in the area of fabric image retrieval, especially the identification and classification of visual features. One of the challenges associated with the domain of content-based image retrieval (CBIR) is the semantic gap between low-level visual features and high-level human perceptions. Generally, CBIR includes two main components, namely feature extraction and similarity measurement. Therefore, this research aims to determine the content-based image retrieval for fabric using feature extraction techniques grouped into tradi

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Tena, Silvester, Rudy Hartanto, and Igi Ardiyanto. "Content-based image retrieval for fabric images: A survey." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (2021): 1861–72. https://doi.org/10.11591/ijeecs.v23.i3.pp1861-1872.

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In recent years, a great deal of research has been conducted in the area of fabric image retrieval, especially the identification and classification of visual features. One of the challenges associated with the domain of content-based image retrieval (CBIR) is the semantic gap between low-level visual features and high-level human perceptions. Generally, CBIR includes two main components, namely feature extraction and similarity measurement. Therefore, this research aims to determine the content-based image retrieval for fabric using feature extraction techniques grouped into traditional metho

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Masood, Anum, Muhammad Alyas Shahid, and Muhammad Sharif. "Content-Based Image Retrieval Features: A Survey." International Journal of Advanced Networking Applications 10, no. 01 (2018): 3741–57. http://dx.doi.org/10.35444/ijana.2018.100111.

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Zhang, Chi, and Lei Huang. "Content-Based Image Retrieval Using Multiple Features." Journal of Computing and Information Technology 22, LISS 2013 (2014): 1. http://dx.doi.org/10.2498/cit.1002256.

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Xu, Bing. "Retrieval Model Based on Image Content Features." Applied Mechanics and Materials 635-637 (September 2014): 1035–38. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1035.

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This paper presents fuzzy color histogram feature-based image retrieval method and texture spectrum fuzzy histogram feature analyzes the image database indexing techniques and the introduction of the experimental system for an improved method of fuzzy indexes. Algorithm reflects the underlying characteristics of high-level concepts and integration, relevance feedback and machine learning mechanism combining ideas. In this paper, the algorithm full processing power of computer systems, has a certain reference value and practical significance.

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Alex, Akshay, Pranay Goyal, Tejaswinee Thorat, Mayur Sonawane, and Mr Subhash Rathod. "Content Based Image Retrieval Using Spatial Features." International Journal of Engineering Trends and Technology 8, no. 6 (2014): 313–18. http://dx.doi.org/10.14445/22315381/ijett-v8p258.

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Ingle, Darshan, and Shalini Bhatia. "Content based Image Retrieval using Combined Features." International Journal of Computer Applications 44, no. 17 (2012): 31–34. http://dx.doi.org/10.5120/6358-8801.

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Reddy, P. V. N., and K. Satya Prasad. "Multiwavelet based Texture Features for Content based Image Retrieval." International Journal of Computer Applications 17, no. 1 (2011): 39–44. http://dx.doi.org/10.5120/2182-2753.

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Mustaffa, Mas Rina, Fatimah Ahmad, Rahmita Wirza O.K. Rahmat, and Ramlan Mahmod. "Content-Based Image Retrieval Based On Color-Spatial Features." Malaysian Journal of Computer Science 21, no. 1 (2008): 1–12. http://dx.doi.org/10.22452/mjcs.vol21no1.1.

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Zhou, Xiang Sean, and Thomas S. Huang. "Edge-based structural features for content-based image retrieval." Pattern Recognition Letters 22, no. 5 (2001): 457–68. http://dx.doi.org/10.1016/s0167-8655(00)00124-0.

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Dr., M. Vadivukarassi, and G. JawaherlalNehru Dr. "Efficient Content Based Image Retrieval Analysis of Distance Matrices." IJCSET MAY Volume 9 Issue 5 9, no. 5 (2023): 1–4. https://doi.org/10.5281/zenodo.8382651.

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In this paper, we proposed a new method of feature extraction to improve the efficiency for retrieving the JPEG Compressed Images. We extract two DCT features, namely DC feature and AC feature, from the compressed image. Then we measure the image distance between the query image and the images in the database using these DCT features. Our retrieval system will give rank to the retrieved database images to define its similarity with the query image. Our proposed system does not need to full decoding, it only needs partial entropy decoding. Therefore, our proposed system takes less time for retr

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AlSaidi, B. K., B. J. Al-Khafaji, and Wahab S. A. Al. "Content Based Image Clustering Technique Using Statistical Features and Genetic Algorithm." Engineering, Technology & Applied Science Research 9, no. 2 (2019): 3892–95. https://doi.org/10.5281/zenodo.2647709.

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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 extr

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Esther Ratna, T., and N. Subash Chandra. "Binary Plane Technique Based Color Quantization for Content Based Image Retrieval." International Journal of Engineering & Technology 7, no. 3.1 (2018): 124. http://dx.doi.org/10.14419/ijet.v7i3.1.16814.

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Extracting accurate informative file from a high volume of graphic files is a challenging task. This paper focus on presenting a new color indexing approach using the histogram features. Two histogram features like maximum color histogram and minimum color histogram are computed and are vector quantized to constitute a feature vector. Bit plane technique is used to map these features based upon it value at the respective position. The ultimate goal of any retrieval method is to attain higher precision within a short span of time that could be achieved if the data is in compressed to accomplish

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Kubo, Masaaki, Zaher Aghbari, Akifumi Makinouchi, and Kun-Seok Oh. "Content-Based Image Retrieval Technique Using Wavelet-Based Shift and Brightness Invariant Edge Feature." International Journal of Wavelets, Multiresolution and Information Processing 01, no. 02 (2003): 163–78. http://dx.doi.org/10.1142/s0219691303000141.

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This paper proposes a technique for indexing, clustering and retrieving images based on their edge features. In this technique, images are decomposed into several frequency bands using the Haar wavelet transform. From the one-level decomposition sub-bands an edge image is formed. Next, the higher order autocorrelation function is applied on the edge image to extract the edge features. These higher order autocorrelation features are normalized to generate a compact feature vector, which is invariant to shift, image size and gray level. Then, these feature vectors are clustered by a self-organiz

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Singh, Bohar, and Mrs Mehak Aggarwal. "Knn And Steerable Pyramid Based Enhanced Content Based Image Retrieval Mechanism." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 17, no. 2 (2018): 7215–25. http://dx.doi.org/10.24297/ijct.v17i2.7606.

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Recently, digital content has become a significant and inevitable asset of or any enterprise and the need for visual content management is on the rise as well. There has been an increase in attention towards the automated management and retrieval of digital images owing to the drastic development in the number and size of image databases. A significant and increasingly popular approach that aids in the retrieval of image data from a huge collection is called Content-based image retrieval (CBIR). Content-based image retrieval has attracted voluminous research in the last decade paving way for d

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Dr., Aziz Makandar, Rashmi Somshekhar Mrs., and Nayan Jadav Miss. "Content Based Image Retrieval." International Journal of Trend in Scientific Research and Development 3, no. 4 (2019): 1151–54. https://doi.org/10.31142/ijtsrd24047.

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The incremented desideratum of content based image retrieval system can be found in a number of different domains such as Data Mining, Edification, Medical Imaging, Malefaction Aversion, climate, Remote Sensing and Management of Globe Resources. Google's image search and photo album implements such as image search, Google's Picasa project applications in general gregarious networking environment, the hunt for practical, efficacious image search in the web context. Our application provides the color based image retrieval, utilizing features like dominant color. The color features are ob

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Sheela, Kalavakuri. "Content Based Image Retrieval Using Colour and Shape Features." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (2025): 731–35. https://doi.org/10.22214/ijraset.2025.66916.

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Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Content based image retrieval (CBIR) deals with the extraction of implicit knowledge from the image database. Feature selection and extraction is the pre-processing step of CBIR. Obviously this is a critical step in the entire scenario of CBIR. Though there are various features available, the aim is to identify the best features and thereby extract relevant information from the images

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Mahajan, Vipul R., and Alka Khade. "A Survey: Content Based Image Retrieval using Block Truncation Coding." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 12 (2018): 46. http://dx.doi.org/10.23956/ijarcsse.v7i12.495.

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A new approach to index color images using the features extracted from the error diffusion Block truncation coding (EDBTC). The EDBTC produces two color quantizes and a bitmap Image, which is further, managed using vector quantization (VQ) to create the image feature Descriptor. Herein two features are presented namely, colour histogram feature (CHF),bit Pattern histogram feature (BHF) to measure the similarity between a query image and the Target image in database. The CHF and BHF are calculated from the VQ-indexed color quantized and VQ- indexed bitmap image, respectively. The distance calcu

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MORE, MAHADEV A. "CONTENT BASED IMAGE RETRIVAL USING DIFFERENT CLUSTERING TECHNIQUES." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 09 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem25835.

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CBIR (Content based image retrieval) is the software system for retrieving the images from the database by using their features. In CBIR technique, the images are retrieved from the dataset by using the features like color, text, shape,texture and similarity. Object recognition technique is used in CBIR. Research on multimedia systems and content-based image retrieval is given tremendous importance during the last decade. The reason behind this is the fact that multimedia databases handle text, audio, video and image information, which are of prime interest in web and other high end user appli

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LIN, RUEI-SHIANG, and LING-HWEI CHEN. "CONTENT-BASED AUDIO RETRIEVAL BASED ON GABOR WAVELET FILTERING." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 06 (2005): 823–37. http://dx.doi.org/10.1142/s0218001405004290.

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Rapid increase in the amount of audio data and especially music collections demand an efficient method to automatically retrieve audio objects based on its content. In this paper, based on the Gabor wavelet features, we will propose a method for content-based retrieval of perceptually similar music pieces in audio documents. It allows the user to select a reference passage within an audio file and retrieve perceptually similar passages such as repeating phrases within a music piece, similar music clips in a database or one song sung by different persons or in different languages. The proposed

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Akmal, Akmal, Rinaldi Munir, and Judhi Santoso. "Automatic Weight of Color, Texture, and Shape Features in Content-Based Image Retrieval Using Artificial Neural Network." JOIV : International Journal on Informatics Visualization 7, no. 3 (2023): 665. http://dx.doi.org/10.30630/joiv.7.3.1184.

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Image retrieval is the process of finding images in the database that are similar to the query image by measuring how close the feature values of the query image are to other images. Image retrieval is currently dominated by approaches that combine several different representations or features. The optimal weight of each feature is needed in combining the image features such as color features, texture features, and shape features. In this study, we use a multi-layer perceptron artificial neural network (MLP) method to obtain feature weights automatically and simultaneously look for optimal wei

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Pathak, Debanjan, and U. S. N. Raju. "Content-based image retrieval using feature-fusion of GroupNormalized-Inception-Darknet-53 features and handcraft features." Optik 246 (November 2021): 167754. http://dx.doi.org/10.1016/j.ijleo.2021.167754.

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Lande, Milind Vijayrao, Prof PraveenBhanodiya, and Mr Pritesh Jain. "Analysis and Comparison of Color Features for Content Based Image Retrieval." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 2 (2005): 522–27. http://dx.doi.org/10.24297/ijct.v4i2b2.3314.

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Creation of a content-based image retrieval system implies solving a number of difficult problems, including analysis of low-level image features and construction of feature vectors, multidimensional indexing, design of user interface, and data visualization. Color is one of the important features used in CBIR systems. The methods of characterizing color fall into two major categories:Â Histograms and Statistical. An experimental comparison of a number of different color features for content-based image retrieval presented in these paper. The primary goal is to determine which color feature is

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Yin, DongSheng, and DeBo Liu. "Content-Based Image Retrial Based on Hadoop." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/684615.

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Generally, time complexity of algorithms for content-based image retrial is extremely high. In order to retrieve images on large-scale databases efficiently, a new way for retrieving based on Hadoop distributed framework is proposed. Firstly, a database of images features is built by using Speeded Up Robust Features algorithm and Locality-Sensitive Hashing and then perform the search on Hadoop platform in a parallel way specially designed. Considerable experimental results show that it is able to retrieve images based on content on large-scale cluster and image sets effectively.

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Salih, Fawzi Abdul Azeez, and Alan Anwer Abdulla. "An Efficient Two-layer based Technique for Content-based Image Retrieval." UHD Journal of Science and Technology 5, no. 1 (2021): 28–40. http://dx.doi.org/10.21928/uhdjst.v5n1y2021.pp28-40.

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The rapid advancement and exponential evolution in the multimedia applications raised the attentional research on content-based image retrieval (CBIR). The technique has a significant role for searching and finding similar images to the query image through extracting the visual features. In this paper, an approach of two layers of search has been developed which is known as two-layer based CBIR. The first layer is concerned with comparing the query image to all images in the dataset depending on extracting the local feature using bag of features (BoF) mechanism which leads to retrieve certain

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Varsha Kiran Patil, Shristi Manoj Dhamange, Shraddha Anil Bhandurge, and Samruddhi Udaysinh Gaikwad. "Conceptional review of the Content-based Image retrieval." International Journal of Science and Research Archive 10, no. 1 (2023): 316–29. http://dx.doi.org/10.30574/ijsra.2023.10.1.0733.

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This article examines the process of the Content-based Image Retrieval (CBIR ) system , the features and steps involved, the methods employed, and the applications. CBIR aims to identify, sort, and manage images on the basis of the requirements posed to the system. The system cross checks the requirements using Machine learning processes against a given database and reduces the manual effort of sifting through all pictures individually. Incorporating references to the different methods of feature extraction, this article emphasizes understanding which features are considered important and the

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LIN, HWEI-JEN, YANG-TA KAO, FU-WEN YANG, and PATRICK S. P. WANG. "CONTENT-BASED IMAGE RETRIEVAL TRAINED BY ADABOOST FOR MOBILE APPLICATION." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 04 (2006): 525–41. http://dx.doi.org/10.1142/s021800140600482x.

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This paper proposes a Content-Based Image Retrieval (CBIR) system applicable in mobile devices. Due to the fact that different queries to a content-based image retrieval (CBIR) system emphasize different subsets of a large collection of features, most CBIR systems using only a few features are therefore only suitable for retrieving certain types of images. In this research we combine a wide range of features, including edge information, texture energy, and the HSV color distributions, forming a feature space of up to 1053 dimensions, in which the system can search for features most desired by

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Mir, Nighat. "Content-Based Web Watermarking." International Journal of Knowledge Society Research 4, no. 3 (2013): 79–88. http://dx.doi.org/10.4018/ijksr.2013070107.

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With the increasing growth in internet use, information has turned into e-information and provides a greater freedom for information sharing. This also brings a greater need for information security by protecting copyrights of online authors. A novel tamper proof web watermarking technique has been proposed in this paper that uses the textual features of different languages. Initially three different languages (English, Urdu and Arabic) have been used in this research for testing and verification, however this idea can be extended to other languages. Watermarks are generated based on language

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Deldjoo, Yashar, Mehdi Elahi, Paolo Cremonesi, Franca Garzotto, Pietro Piazzolla, and Massimo Quadrana. "Content-Based Video Recommendation System Based on Stylistic Visual Features." Journal on Data Semantics 5, no. 2 (2016): 99–113. http://dx.doi.org/10.1007/s13740-016-0060-9.

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Kumar, Sumit. "A Content Based Image Retrieval Mechanism Based on Primitive Features." Computology: Journal of Applied Computer Science and Intelligent Technologies 2, no. 2 (2022): 23–32. http://dx.doi.org/10.17492/computology.v2i2.2203.

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ZHEN, RONG, MING-QUAN ZHOU, NA WEI, and GUO-HUA GENG. "CONTENT-BASED RETRIEVAL IN ARCHEOLOGY." International Journal of Information Acquisition 03, no. 01 (2006): 69–75. http://dx.doi.org/10.1142/s0219878906000848.

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Retrieving images from large and varied collections using image content is a challenging problem. This paper presents a novel method for cultural relic image retrieval algorithm by integration of multiple cues. The preprocessing of the images makes it possible for correct retrieval and classification of images even when they are deformed by transformation of rotation, scaling and translation or a combination of these. In feature representation, a method combining both color and shape features of image is proposed to improve the retrieval performance. A modified principal component analysis is

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Rahman, Arif, Edi Winarko, and Khabib Mustofa. "Content-based product image retrieval using squared-hinge loss trained convolutional neural networks." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (2023): 5804–12. https://doi.org/10.11591/ijece.v13i5.pp5804-5812.

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Convolutional neural networks (CNN) have proven to be highly effective in large-scale object detection and image classification, as well as in serving as feature extractors for content-based image retrieval. While CNN models are typically trained with category label supervision and softmax loss for product image retrieval, we propose a different approach for feature extraction using the squared-hinge loss, an alternative multiclass classification loss function. First, transfer learning is performed on a pre-trained model, followed by finetuning the model. Then, image features are extracted bas

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Qizi, Shermamatova Zaynab Azimjon, Asatullayeva Umida, Olimjonova Sarvinoz, and Xafizova Maqsuda. "Features and main characteristics of content-based instruction." ACADEMICIA: An International Multidisciplinary Research Journal 12, no. 5 (2022): 1132–35. http://dx.doi.org/10.5958/2249-7137.2022.00525.0.

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Lai, Chun-Ming, Mei-Hua Chen, Endah Kristiani, Vinod Kumar Verma, and Chao-Tung Yang. "Fake News Classification Based on Content Level Features." Applied Sciences 12, no. 3 (2022): 1116. http://dx.doi.org/10.3390/app12031116.

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Due to the openness and easy accessibility of online social media (OSM), anyone can easily contribute a simple paragraph of text to express their opinion on an article that they have seen. Without access control mechanisms, it has been reported that there are many suspicious messages and accounts spreading across multiple platforms. Accordingly, identifying and labeling fake news is a demanding problem due to the massive amount of heterogeneous content. In essence, the functions of machine learning (ML) and natural language processing (NLP) are to enhance, speed up, and automate the analytical

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Mahajan, A. D., and S. Chaudhary. "Hybrid Features For Content Based Image Retrieval System." International Journal of Computer Sciences and Engineering 6, no. 10 (2018): 11–15. http://dx.doi.org/10.26438/ijcse/v6i10.1115.

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Alizadeh, Meysam, Jacob N. Shapiro, Cody Buntain, and Joshua A. Tucker. "Content-based features predict social media influence operations." Science Advances 6, no. 30 (2020): eabb5824. http://dx.doi.org/10.1126/sciadv.abb5824.

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We study how easy it is to distinguish influence operations from organic social media activity by assessing the performance of a platform-agnostic machine learning approach. Our method uses public activity to detect content that is part of coordinated influence operations based on human-interpretable features derived solely from content. We test this method on publicly available Twitter data on Chinese, Russian, and Venezuelan troll activity targeting the United States, as well as the Reddit dataset of Russian influence efforts. To assess how well content-based features distinguish these influ

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Sumalatha, L., V. Venkata Krishna, and A. Vinay Babu. "Image Content Authentication based on Wavelet Edge Features." International Journal of Computer Applications 49, no. 23 (2012): 24–29. http://dx.doi.org/10.5120/7944-1281.

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Mu, Xiangming. "Semantic visual features in content-based video retrieval." Proceedings of the American Society for Information Science and Technology 43, no. 1 (2007): 1–14. http://dx.doi.org/10.1002/meet.1450430153.

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Mo, Chengyu, Fenlin Liu, Ma Zhu, Gengcong Yan, Baojun Qi, and Chunfang Yang. "Image Steganalysis Based on Deep Content Features Clustering." Computers, Materials & Continua 76, no. 3 (2023): 2921–36. http://dx.doi.org/10.32604/cmc.2023.039540.

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Kugunavar, Sneha, and Prabhakar C. J. "Content-Based Medical Image Retrieval Using Delaunay Triangulation Segmentation Technique." Journal of Information Technology Research 14, no. 2 (2021): 48–66. http://dx.doi.org/10.4018/jitr.2021040103.

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Abstract:

This article presents a novel technique for retrieval of lung images from the collection of medical CT images. The proposed content-based medical image retrieval (CBMIR) technique uses an automated image segmentation technique called Delaunay triangulation (DT) in order to segment lung organ (region of interest) from the original medical image. The proposed method extracts novel and discriminant features from the segmented lung region instead of extracting novel features from the whole original image. For the extraction of shape features, the authors employ edge histogram descriptor (EHD) and

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Hassan, Rasha Qassim, Zainab N. Sultani, and Ban N. Dhannoon. "Content-based image retrieval based on corel dataset using deep learning." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1854. http://dx.doi.org/10.11591/ijai.v12.i4.pp1854-1863.

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A popular technique for retrieving images from huge and unlabeled image databases are content-based-image-retrieval (CBIR). However, the traditional information retrieval techniques do not satisfy users in terms of time consumption and accuracy. Additionally, the number of images accessible to users are growing due to web development and transmission networks. As the result, huge digital image creation occurs in many places. Therefore, quick access to these huge image databases and retrieving images like a query image from these huge image collections provides significant challenges and the ne

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49

Hassan, Rasha Qassim, Zainab N. Sultani, and Ban N. Dhannoon. "Content-based image retrieval based on corel dataset using deep learning." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1854–63. https://doi.org/10.11591/ijai.v12.i4.pp1854-1863.

Full text

Abstract:

A popular technique for retrieving images from huge and unlabeled image databases are content-based-image-retrieval (CBIR). However, the traditional information retrieval techniques do not satisfy users in terms of time consumption and accuracy. Additionally, the number of images accessible to users are growing due to web development and transmission networks. As the result, huge digital image creation occurs in many places. Therefore, quick access to these huge image databases and retrieving images like a query image from these huge image collections provides significant challenges and the ne

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Afifi, Ahmed J., and Wesam M. Ashour. "Image Retrieval Based on Content Using Color Feature." ISRN Computer Graphics 2012 (March 15, 2012): 1–11. http://dx.doi.org/10.5402/2012/248285.

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Content-based image retrieval from large resources has become an area of wide interest in many applications. In this paper we present a CBIR system that uses Ranklet Transform and the color feature as a visual feature to represent the images. Ranklet Transform is proposed as a preprocessing step to make the image invariant to rotation and any image enhancement operations. To speed up the retrieval time, images are clustered according to their features using k-means clustering algorithm.

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