The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Salient region detection by modeling distributions of color and orientation
IEEE Transactions on Multimedia
Esaliency (Extended Saliency): Meaningful Attention Using Stochastic Image Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
A biologically inspired computational model for image saliency detection
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Hi-index | 0.00 |
Saliency detection is useful for high level applications such as adaptive compression, image retargeting, object recognition, etc. In this paper, we introduce an effective region-based solution for saliency detection. We first use the adaptive mean shift algorithm to extract superpixels from the input image, then apply Gaussian Mixture Model (GMM) to cluster superpixels based on their color similarity, and finally calculate the saliency value for each cluster using compactness metric together with modified PageRank propagation. This solution is able to represent the image in a perceptually meaningful way and is robust to over-segmentation. It highlights salient regions with full resolution, well-defined boundary. Experimental results show that both the adaptive mean shift and the modified PageRank algorithm contribute substantially to the saliency detection result. In addition, the ROC analysis demonstrates that our approach significantly outperforms five existing popular methods.