Image Retrieval Using Salient Points And Geometric Hashing

In image retrieval, global features related to color or texture are commonly used to describe the image content. The problem with this approach is that these global features cannot capture all parts of the image having different characteristics. Therefore, local computation of image information is necessary. By using salient points to represent local information, more discriminative features can be computed.This paper presents a content-based image retrieval technique based on interest points matching and geometric hashing. We estimate points with wavelet transform as salient points. A small region around the salient point is located as an image patch. Low-level features are extracted to describe each image patch. To provide geometric invariant image matching, we index the image patches into a 2-D hash table by geometric hashing technique. Thus, the matching is invariant to global and local geometric transforms. In addition, since we use the image patch to capture the local information, the indexing can effectively handle partial matching. We formulate a matching criterion by weighted voting technique to incorporate the spatial interrelationship into consideration. We have performed a series of experiments to confirm the effectiveness of our method. Images are globally transformed and locally manipulated to examine the efficiency of our indexing scheme. Experimental results indicate satisfactory retrieval in the case of partial matching and geometric transformation.