COLOR VS. TEXTURE FEATURE EXTRACTION AND MATCHING IN VISUAL CONTENT RETRIEVAL BY USING GLOBAL COLOR HISTOGRAM
P S Shimi · 2014
Content Based Image Retrieval (CBIR) is the technique which uses visual contents to search images from large database. Image retrieval is achieved according to the similarity of image features. Color and texture is the most important visual features. CBIR systems focused on using low-level features like color, texture and shape for image representation. The color and texture features can be extracted using global color histogram. This paper measures the performance of color and textual statistical features of an image in retrieving the similar images from the data set. The performance is measured by implementing a single CBIR system in two levels. The first level uses color features for image retrieval. The second level uses the texture features for similar image extraction. Euclidean distance is used as the distance measure of two images. It is found that the system which uses texture features retrieves the most similar images from the data set.