This paper presents a content-based image retrieval technique based on interest points matching and geometric hashing. We estimate points with significant luminance variations as interest points. A small region around the interest 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.
With the advance of multimedia technologies and the explosive expansion of the World Wide Web, the volume of image and video data increases rapidly. An efficient and effective multimedia data retrieval technique is needed. In this paper, we propose an approach based on feature points for the content-based image retrieval. The feature points extracted from the multiresolution representation of the query image and database image are first matched to determine the matching pairs. Then, the marching pairs are classified into groups. Finally, two similarity measurements based on different similarity requirements are proposed to compute the similarity degree. We perform a series of experiments to study the characteristics of this approach, and compare with the region-based approach on similar-shot sequence retrieval. The comparison shows the superiority of this approach.
Image registration is the fundamental task used to match two or more partially overlapping images and stitch these images into one panoramic image comprising the whole scene. To register two images, the coordinate transformation between a pair of images must be found. In this paper, a multiresolution feature-based method is developed to efficiently estimate an eight-parametric projective transformation model between pairs of images. The proposed method has been tested and work well on many images of static scene taken with a hand-held digital still camera.
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