TY - GEN
T1 - Salient-SIFT for image retrieval
AU - Liang, Zhen
AU - Fu, Hong
AU - Chi, Zheru
AU - Feng, Dagan
PY - 2010/12/1
Y1 - 2010/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=78650917937&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-17688-3_7
DO - 10.1007/978-3-642-17688-3_7
M3 - Conference article published in proceeding or book
SN - 3642176879
SN - 9783642176876
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 62
EP - 71
BT - Advanced Concepts for Intelligent Vision Systems - 12th International Conference, ACIVS 2010, Proceedings
T2 - 12th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2010
Y2 - 13 December 2010 through 16 December 2010
ER -