We propose a novel threshold-free salient object detection approach which integrates both saliency density and edge response. The salient object with a well-defined boundary can be automatically detected by our approach. Saliency density and edge response maximization is used as the quality function to direct the salient object discovery. The global optimal window containing a salient object is efficiently located through the proposed saliency density and edge response based branch-and-bound search. To extract the salient object with a well-defined boundary, the GrabCut method is applied, initialized by the located window. Experimental results show that our approach outperforms the methods only using saliency or edge response and achieves a comparable performance with the best state-of-the-art method, while being without any threshold or multiple iterations of GrabCut.

The copy-move or region duplication forgery technique is a very common type of image manipulation, where a region of the image is copied and then pasted in the same image in order to hide some details. In this paper, a keypoint-based method for copy-move forgery detection is proposed. Firstly, the feature points are detected from the image by using the Förstner Operator. Secondly, the algorithm extracts the features by using MROGH feature descriptor, and then matching the features. Finally, the affine transformation parameters can be estimated using the RANSAC algorithm. Experimental results are presented to confirm that the proposed method is effective to locate the altered region with geometric transformation (rotation and scaling).

Existing salient object detection methods either simply use a threshold to detect desired salient objects from saliency map or search the most promising rectangular window covering salient objects on the saliency map. There are two problems in the existing methods: 1) The performance of threshold-dependent methods depends on a threshold selection and it is difficult to select an appropriate threshold value. 2) The rectangular window not only covers the salient object but also contains background pixels, which leads to imprecise salient object detection. For solving these problems, a novel saliency threshold-free method for detecting the salient object with a well-defined boundary is proposed in this paper. We propose a novel window search algorithm to locate a rectangular window on our saliency map, which contains as many as possible pixels belonging the salient object and as few as possible background pixels. Once the window is determined, GrabCut is applied to extract salient object with a well-defined boundary. Compared with existing methods, our approach doesn't need any threshold to binarize the saliency map and additional operations. Experimental results show that our approach outperforms 4 state-of-the-art salient object detection methods, yielding higher precision and better F-Measure.

The research of detecting co-saliency over multiple images is just beginning. The existing methods multiply the saliency on single image by the correspondence over multiple images to estimate co-saliency. They have difficulty in highlighting the co-salient object that is not salient on single image. It is caused by two problems. (1) The correspondence computation lacks precision. (2) The co-saliency multiplication formulation does not fully consider the effect of correspondence for co-saliency. In this paper, we propose a novel co-saliency detection scheme linearly combining foreground correspondence and single-view saliency. The progressive graph matching based foreground correspondence method is proposed to improve the precision of correspondence computation. Then the foreground correspondence is linearly combined with single-view saliency to compute co-saliency. According to the linear combination formulation, high correspondence could bring about high co-saliency, even when single-view saliency is low. Experiments show that our method outperforms previous state-of-the-art co-saliency methods.

We propose a novel saliency detection model based on Bayes' theorem. The model integrates the two parts of Bayes' equation to measure saliency, each part of which was considered separately in the previous models. The proposed model measures saliency by computing local kernel density estimation of features in the center-surround region and global kernel density estimation of features at each pixel across the whole image. Under the proposed model, a saliency detection method is presented that extracts DCT (Discrete Cosine Transform) magnitude of local region around each pixel as the feature. Experiments show that the proposed model not only performs competitively on psychological patterns and better than the current state-of-the-art models on human visual fixation data, but also is robust against signal uncertainty.

BRISK (Binary Robust Invariant Scalable Keypoints) works dramatically faster than well-established algorithms (SIFT and SURF) while maintaining matching performance. However BRISK relies on intensity, color information in the image is ignored. In view of the importance of color information in vision applications, we propose CBRISK, a novel method for taking into account color information during keypoint detection and description. Instead of grayscale intensity image, the proposed approach detects keypoints in the photometric invariant color space. On the basis of binary intensity BRISK (original BRISK) descriptor, the proposed approach embeds binary invariant color presentation in the CBRISK descriptors. Experimental results show that CBRISK is more discriminative and robust than BRISK with respect to photometric variation.