Local descriptors have been wildly explored and utilized in image retrieval because of their transformation invariance. In this paper, we propose an improved set of features extarcted from local descriptors for more effective and efficient image retrieval. We propose a salient region selection method to detect human's Region Of Interest (hROI) from an image, which incorporates the Canny edge algorithm and the convex hull method into Itti's saliency model for obtaining hROI's. Our approach is a purely bottom-up process with better robustness. The salient region is used as a window to select the most distinctive features out of the Scale-Invariant Feature Transform (SIFT) features. Our proposed SIFT local descriptors is termed as salient-SIFT features. Experiment results show that the salient-SIFT features can characterize the human perception well and achieve better image retrieval performance than the original SIFT descriptors while the computational complexity is greatly reduced.
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||12th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2010|
|Period||13/12/10 → 16/12/10|
- Theoretical Computer Science
- Computer Science(all)