Saliency Region Detection Improved by Principle Component Analysis and Boundary Information.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

PubMedID: 23744683

Wu PH, Chen CC, Ding JJ, Hsu CY, Huang YW. Saliency Region Detection Improved by Principle Component Analysis and Boundary Information. IEEE Trans Image Process. 2013;.
Salient region detection is useful for several image-processing applications, such as adaptive compression, object recognition, image retrieval, filter design, and image retargeting. A novel method to determine the salient regions of images is proposed in this paper. The L0 smoothing filter and Principle Component Analysis (PCA) play important roles in our framework. The L0 filter is extremely helpful in characterizing fundamental image constituents, i.e., salient edges, and can simultaneously diminish insignificant details, thus producing more accurate boundary information for background merging and boundary scoring. PCA can reduce computational complexity as well as attenuate noise and translation errors. A local-global contrast is then used to calculate the distinction. Finally, image segmentation is used to achieve full-resolution saliency maps. The proposed method is compared with other state-of-the-art saliency detection methods and shown to yield higher precision-recall rates and F-measures.