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 in recent years. 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 [1] to incrementally supply more information, they may fail due to the lack of higher-level information about what exactly was of interest. Since automatic segmentation of Region-of-Interest (ROI) is not always reliable, human assistance is necessary. In this paper, a novel approach combining user defined Region-of-Interest and spatial layout is proposed for CBIR. Better capture of image object is achieved by the user rather than the computer. Therefore, more accurate relevance feedback is achieved and thus leads to a more powerful search engine.