DocumentCode
2570639
Title
Salient region extraction based on intensity mapping for image retrieval
Author
Congyan, Lang ; Xu De ; Ning, Li ; Songhe, Feng
Author_Institution
Inst. of Comput. Sci. & Eng., Beijing Jiaotong Univ., Beijing, China
fYear
2009
fDate
11-14 Oct. 2009
Firstpage
2177
Lastpage
2180
Abstract
Salient region extraction provides an alternative methodology to image description in many applications such as adaptive content delivery and image retrieval. In this paper, we propose a robust approach to extracting the salient region based on bottom-up visual attention. The main contributions are twofold: 1) Instead of the feature parallel integration, the proposed saliencies are derived by serial processing between texture and color feature. 2) A constructive approach is proposed for rendering an image by a non-linear intensity mapping, which can efficiently eliminate high contrast noise regions in the image. And then the salient map can be robustly generated for a variety of nature images. Finally, the salient region extracted by our algorithm is used for image semantic retrieval. Experiments show that the proposed algorithm can characterize the human perception well and achieve satisfied retrieval performance.
Keywords
feature extraction; image denoising; image retrieval; image texture; adaptive content delivery; constructive approach; feature parallel integration; high contrast noise region elimination; human perception; image retrieval intensity mapping; image semantic retrieval; nonlinear intensity mapping; salient region extraction; serial processing; Biology computing; Colored noise; Computational modeling; Content based retrieval; Data mining; Humans; Image coding; Image generation; Image retrieval; Noise robustness;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
Conference_Location
San Antonio, TX
ISSN
1062-922X
Print_ISBN
978-1-4244-2793-2
Electronic_ISBN
1062-922X
Type
conf
DOI
10.1109/ICSMC.2009.5346244
Filename
5346244
Link To Document