Apostol Natsev, Rajeev Rastogi, Kyuseok Shim
1999 SIGMOD record  
Traditional approaches for content-based image querying typically compute a single signature for each image based on color histograms, texture, wavelet transforms, etc. The images returned as the query result are then the ones whose signatures are closest to the signature of the query image. While efficient for simple images, such methods do not work well for complex scenes since these methods fail to retrieve images that match the query only partially, that is, only certain regions of the
more » ... regions of the image match. This inefficiency leads to discarding of images that may be semantically very similar to the query image since they contain the same objects. The problem becomes even more apparent when we consider scaled or translated versions of the similar objects. In this paper, we propose WALRUS (WAveLet-based Retrieval of User-specified Scenes), a novel similarity retrieval algorithm that is robust to scaling and translation of objects within an image. WALRUS employs a novel similarity model in which each image is first decomposed into its regions, and the similarity measure between a pair of images is then defined to be the fraction of the area of the two images covered by matching regions from the images. In order to extract regions for an image, WALRUS considers sliding windows of varying sizes and then clusters them based on the proximity of their signatures. An efficient dynamic programming algorithm is used to compute wavelet-based signatures for the sliding windows. Experimental results on real-life data sets corroborate the effectiveness of WALRUS's similarity model.
doi:10.1145/304181.304217 fatcat:f4mvy5yhgba4biae36wfbr5kwu