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Content-Based Image Retrieval for Textile Dataset and Classification of Fabric Type Using SVM
Published in Springer Singapore
Volume: 1014
Pages: 304 - 314
In this paper, a CBIR system based on multiple features of the images is proposed to retrieve similar images from the database of textile industry. Multiple feature technique includes well-structured union of various color, shape, and texture features. Nowadays, most of the CBIR systems use query-by-example where image is given as a query to the system which extracts the most relevant image from the database having similar features as that of query image. As classifying the category in which a particular image belongs is one of the traditional concerns in image retrieval, a lot of approaches are used for classifying large datasets of images. As traditional CBIR techniques fail for some real-world applications where user wants the desired result in the first go. Support vector machine is a learning model which when used with retrieval process finds appropriate images more often. Also, it is useful even when the size of the dataset is not so large. Using SVM classifier, the CBIR system can retrieve more relevant images from the databases. In this paper, the capability of SVM on classification of images is evaluated. © Springer Nature Singapore Pte Ltd. 2020.
About the journal
JournalData powered by TypesetFrontiers in Intelligent Computing: Theory and Applications Advances in Intelligent Systems and Computing
PublisherData powered by TypesetSpringer Singapore
Open AccessNo