Combine User Defined Region-of-Interest and Spatial
Llayout 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 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.
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