Efficient Classification of Images using Histogram based Average Distance Computation Algorithm Extended with Duplicate Image Detection

Internet Image search is a day to day activity performed by user. User enters a keyword in a search engine like Google, Yahoo, Bing etc for retrieval of keyword related images, where millions of images are retrieved through search engines. The problem with a keyword search is that keywords entered by user are very short and ambiguous, hence images which are retrieved are of different categories and some of them are irrelevant. Visual information is used in order to solve the ambiguity in text based image retrieval. User only has to click on one query image. The query image is categorized based on textual features like image title, image URL, context and some visual features like histogram distance computation, SIFT, region based features. The query image selected by the user is first classified into a particular category and the images related to the query image are then retrieved by matching the class of query image and class of other images. Using image clustering, classified images are clustered and the keywords corresponding to the image clusters are extracted. The original keyword is extended by appending the extracted keyword with highest frequency. This gives more detail idea about user’s search intention. The images are then re-ranked using visual and textual similarity metrics. Duplicate images which are retrieved in search results are detected and eliminated by using SURF(Speeded Up Robust Feature) technique. The system is tested on variety of categories like person, scenery images at semantic level and other general categories like general objects, objects with simple background etc. The system is totally web based and works dynamically on any keyword given as input by user.