Content Based Image Retrieval - COMPARATIVE ANALYSIS OF GABOR FEARURE AND HISTORGRAM ORIENTED GRADIENT USING TEXTURE FEATURES FOR CONTENT BASED IMAGE RETRIEVAL

Computer Engineering

Content-based image retrieval is one amongst the difficult problems for obtaining the connected image from a pool of info with giant image databases turning into a reality each in scientific and medical domain and within the large advertising/marketing domain, strategies for organizing a info of pictures and for economical retrieval became vital. An image shape is characterized through features of comprehensive shape, color and texture. Content-based image retrieval (CBIR), additionally referred to as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is that the application of computer vision techniques to the image retrieval issues, that is, the matter of finding out digital pictures in massive databases. The components are being utilized to retrieve image and cataloging the systems. This paper contrasts two strategies established on image retrieval such as Gabor filter and Histogram Oriented Gradient (HOG). These patterns are differentiated via their correctness and accuracy. The results of experiment show that Gabor filter is not more accurate and efficient than HOG.

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