DocumentCode :
1238653
Title :
Boosting color saliency in image feature detection
Author :
Van de Weijer, Joost ; Gevers, Theo ; Bagdanov, Andrew D.
Author_Institution :
Lear Group, GRAVIR-INRIA, Montbonnot, France
Volume :
28
Issue :
1
fYear :
2006
Firstpage :
150
Lastpage :
156
Abstract :
The aim of salient feature detection is to find distinctive local events in images. Salient features are generally determined from the local differential structure of images. They focus on the shape-saliency of the local neighborhood. The majority of these detectors are luminance-based, which has the disadvantage that the distinctiveness of the local color information is completely ignored in determining salient image features. To fully exploit the possibilities of salient point detection in color images, color distinctiveness should be taken into account in addition to shape distinctiveness. In this paper, color distinctiveness is explicitly incorporated into the design of saliency detection. The algorithm, called color saliency boosting, is based on an analysis of the statistics of color image derivatives. Color saliency boosting is designed as a generic method easily adaptable to existing feature detectors. Results show that substantial improvements in information content are acquired by targeting color salient features.
Keywords :
feature extraction; image colour analysis; statistical analysis; color image derivatives; color imaging; color saliency boosting; image feature detection; local color information; salient point detection; statistical analysis; Algorithm design and analysis; Boosting; Computer vision; Detectors; Event detection; Image analysis; Image color analysis; Shape; Statistical analysis; Statistics; Index Terms- Image saliency; color imaging.; feature detection; image statistics; Algorithms; Artificial Intelligence; Color; Colorimetry; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/TPAMI.2006.3
Filename :
1542040
Link To Document :
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