Salient region detection by modeling distributions of color and orientation
IEEE Transactions on Multimedia
Evaluation of Segmentation Techniques Using Region Size and Boundary Information
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Expert Systems with Applications: An International Journal
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Color histogram-based image segmentation
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Evaluation of segmentation techniques using region area and boundary matching information
Journal of Visual Communication and Image Representation
A method for MRI segmentation of brain tissue
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
A new method based on the CLM of the LV RNN for brain MR image segmentation
Digital Signal Processing
Multimedia Tools and Applications
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In this paper, we propose a novel unsupervised algorithm for the segmentation of salient regions in color images. There are three phases in this algorithm. In the first phase, we use nonparametric density estimation to extract candidates of dominant colors in an image, which are then used for the quantization of the image. The label map of the quantized image forms initial regions of segmentation. In the second phase, we define salient region with two properties; i.e., conspicuous; compact and complete. According to the definition, two new parameters are proposed. One is called ldquoImportance indexrdquo, which is used to measure the importance of a region, and the other is called ldquoMerging likelihoodrdquo, which is utilized to measure the suitability of region merging. Initial regions are merged based on the two new parameters. In the third phase, a similarity check is performed to further merge the surviving regions. Experimental results show that the proposed method achieves excellent segmentation performance for most of our test images. In addition, the computation is very efficient.