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- W3083307112 abstract "Content Based Image Retrieval system automatically retrieves the most relevant images to the query image by extracting the visual features instead of keywords from images. Over the years, several researches have been conducted in this field but the system still faces the challenge of semantic gap and subjectivity of human perception. This paper proposes the extraction of low-level visual features by employing color moment, Local Binary Pattern and Canny Edge Detection techniques for extracting color, texture and edge features respectively. The combination of these features is used in conjunction with Support Vector Machine to reduce the retrieval time and improve the overall precision. Also, the challenge of semantic gap between low and high level features is addressed by incorporating Relevance Feedback. Average precision value of 0.782 was obtained by combining the color, texture and edge features, 0.896 was obtained by using combined features with SVM, 0.882 was obtained by using combined features with Relevance Feedback to overcome the challenge of semantic gap. Experimental results exhibit improved performance than other state of the art techniques." @default.
- W3083307112 created "2020-09-11" @default.
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- W3083307112 date "2018-09-30" @default.
- W3083307112 modified "2023-09-23" @default.
- W3083307112 title "CONTENT BASED IMAGE RETRIEVAL SYSTEM BY FUSION OF COLOR, TEXTURE AND EDGE FEATURES WITH SVM CLASSIFIER AND RELEVANCE FEEDBACK" @default.
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- W3083307112 doi "https://doi.org/10.29121/granthaalayah.v6.i9.2018.1230" @default.
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