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An integrated segmentation technique for interactive image retrieval

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2 Author(s)
R. Aditya ; Biomorphic VLSI Inc., Westlake Village, CA, USA ; S. Ghosal

Many content-based image retrieval (CBIR) systems utilize image segmentation for enabling the user to perform object-level database querying. We propose an integrated segmentation technique for interactive image retrieval, that is reasonably accurate and fast. An initial over-segmentation is generated by finding the dominant color modes in the global histogram of the image using the mean-shift algorithm. Edge-based processing is performed at the initial segment boundaries to merge non-obvious segments. Finally segment shapes are regularized using a Hopfield (1985) type neural network to improve their perceptual quality. A scalable implementation is presented for ensuring fast serial execution of the Hopfield network. The entire segmentation process takes less than 10 seconds to segment 128×192 stock photos on a standard workstation

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Image Processing, 2000. Proceedings. 2000 International Conference on  (Volume:3 )

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