Boosting color saliency in image feature detection
Citations Over TimeTop 10% of 2006 papers
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.
Related Papers
- → Optimally Distinct? Understanding the motivation and ability of organizations to pursue optimal distinctiveness (or not)(2022)33 cited
- → Encoding and Retrieval Processes in Distinctiveness Effects: Toward an Integrative Framework(2006)34 cited
- → Just Another Face in the Crowd? Distinctiveness Seeking in Sweden and Britain(2010)8 cited
- → The word mark YELLOW lacks distinctiveness(2016)
- → To Stand Out or Blend In? The Role of Expert Type in the Funding Decisions of Innovation Projects(2020)