An Effective Combined Feature For Web Based Image Retrieval
H.M.R.B Herath, Y.P.R.D Yapa
Index Terms: Content Based Image Retrieval, Computer Vision, Image Retrieval, Image Processing, Web Based Image Retrieval
Abstract: Technology advances as well as the emergence of large scale multimedia applications and the revolution of the World Wide Web has changed the world into a digital age. Anybody can use their mobile phone to take a photo at any time anywhere and upload that image to ever growing image databases. Development of effective techniques for visual and multimedia retrieval systems is one of the most challenging and important directions of the future research. This paper proposes an effective combined feature for web based image retrieval. Frequently used colour and texture features are explored in order to develop a combined feature for this purpose. Widely used three colour features: Colour moments, Colour coherence vector and Colour Correlogram, and three texture features: Grey Level Co-occurrence matrix, Tamura features and Gabor filter were analyzed for their performance. Precision and Recall were used to evaluate the performance of each of these techniques. By comparing precision and recall values the methods that performed best were taken and combined to form a hybrid feature. The developed combined feature was evaluated by developing a web based CBIR system. A web crawler was used to first crawl through Web sites and images found in those sites are downloaded and the combined feature representation technique was used to extract image features. The test results indicated that this web system can be used to index web images with the combined feature representation schema and to find similar images. Random image retrievals using the web system shows that the combined feature can be used to retrieve images belonging to the general image domain. Accuracy of the retrieval can be noted high for natural images like outdoor scenes, images of flowers etc. Also, images which have a similar colour and texture distribution were retrieved as similar, even though the images were belonging to deferent semantic categories. This can be ideal for an artist who wants to retrieve images which are aesthetically similar and not interested in semantic similarity.
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