Combine user defined region-of-interest and spatial layout for image retrieval
Content-based image retrieval (CBIR) is one of the most active research areas. Many visual feature representations have been explored and many systems built. However, in most of current systems, only the global features such as overall color histogram and texture moments are used which ignore the actual composition of the image in terms of internal objects. Although relevance feedback was proposed (Rui and Huang 1998) to incrementally supply more information, this may fail due to the lack of higher-level information about what exactly was of interest. Since automatic segmentation of the region-of-interest (ROI) is not always reliable, human assistance is necessary. In this paper, a novel approach combining a user defined region-of-interest and spatial layout is proposed for CBIR. Better capture of the image object is achieved by the user rather than the computer. Therefore, more accurate relevance feedback is achieved and thus lends to a more powerful search engine.