A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Selection of Discriminative Tracking Features
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Salient region detection using weighted feature maps based on the human visual attention model
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
Automatic Detection of Object of Interest and Tracking in Active Video
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Automatic Detection of Object of Interest and Tracking in Active Video
Journal of Signal Processing Systems
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Detection of saliency regions in images is useful for object based image understanding and object localization. In our work, we investigate a saliency region detection algorithm based on the Human Visual Attention (HVA) model. In the first phase, we use mutual information and Probability-of-Boundary (PoB) for color saliency and edge detection respectively to filter SURF (Speeded Up Robust Features) key feature points found from the image. For the second phase, bipartite feature matching is deployed for further keypoint selection. We perform the two-phase keypoint filtering iteratively and give selected keypoints different weights for their importance. The final trimmed image is a rectangle region which approximates the distribution of remaining keypoints. We conduct our experiments on Corel Photo Library and MIT-CSAIL Objects and Scenes Database and demonstrate the effectiveness of our proposed algorithm.