Color histograms are widely used for content-based image retrieval. Their advantages are e#ciency, and insensitivity to small changes in camera viewpoint. However, a histogram is a coarse characterization of an image, and so images with very di#erent appearances can have similar histograms. We describe a technique for comparing images called histogram refinement, which imposes additional constraints on histogram based matching. Histogram refinement splits the pixels in a given bucket into several classes, based upon some local property. Within a given bucket, only pixels in the same class are compared. We describe a split histogram called a color coherence vector (CCV), which partitions each histogram bucket based on spatial coherence. CCV's can be computed at over 5 images per second on a standard workstation. A database with 15,000 images can be queried using CCV's in under 2 seconds. We demonstrate that histogram refinement can be used to distinguish images whose color histograms ar...

Recently, the web sites such as e-business sites and shopping mall sites deal with a lot of image information. To find a specific image from these image sources, we usually use web search engines or image database engines which rely on keyword only retrievals or color based retrievals with limited search capabilities. This paper presents an intelligent web e-catalog image retrieval system using metadata and user log. We propose the system architecture, the texture and color based image classification and indexing techniques, and representation schemes of user usage patterns. The query can be given by providing keywords, by selecting one or more sample texture patterns, by assigning color values within positional color blocks, or by combining some or all of these factors. The system keeps track of user’s preferences by generating user query logs and automatically adds more search information to subsequent user queries. To demonstrate the usefulness of the proposed system, some experimental results showing recall and precision are also explained.