Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content Based Image Retrieval through Object Extraction and Querying
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Unsupervised Extraction of Salient Region-Descriptors for Content Based Image Retrieval
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
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We propose the segmentation method of the color Image with irregular texture based on the block homogeneity. Recently segmentation often used for the object based image retrieval and in the application it is more important to approximate the regions than to decide precise region boundary. Our approach subdivides an image into the salient regions. First, a color image is divided into blocks and the block homogeneity for each block is computed by using the modified color histogram intersection. The block homogeneity is designed to have the higher value in the center of region with the homogenous colors or texture while to have the lower value near region boundaries. The image represented by the block homogeneity is the gray scale image and a watershed transform is used to generate closed boundary for each region. As the watershed transform generally results in over-segmentation, region merging based on common boundary strength is followed. The proposed method can be applicable for the segmentation in object based image retrieval.