Image retrieval by convex hulls of interest points and SVM-based weighted feedback

To solve the problem of image retrieval method based on annular color histogram, a new image characteristics extraction method based on convex hulls of interest points is presented. Firstly, the interest points on an image are detected by wavelet transform. Then, convex hulls of interest points are calculated recursively and these points are assigned to some buckets by a specific algorithm to form a color histogram for every bucket. The similarity of two images is calculated by the similarity between histograms of two buckets. Combined with spatial distribution feature and Gabor texture feature based on convex hulls of interest points, the system of image retrieval is built. Experiments on image database show that this method works well when isolated points exist in the interest points set and so provide more accurate retrieval performance comparing with other retrieval method based on interest points. Further more, a novel relevance feedback method is presented. It improves the query point movement relevance feedback method by setting weights based on support vector machine cluster results. The experiments show that by using this method combined with the image retrieval method based on convex hulls of interest points, the precision and recall can be improved about 20% and 10% respectively.