Salient Region Detection via High-Dimensional Color Transform and Local Spatial Support

In this paper, we introduce a novel approach to automatically detect salient regions in an image. Our approach consists of global and local features, which complement each other to compute a saliency map. The first key idea of our work is to create a saliency map of an image by using a linear combin...

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Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 25(2016), 1 vom: 24. Jan., Seite 9-23
1. Verfasser: Kim, Jiwhan (VerfasserIn)
Weitere Verfasser: Han, Dongyoon, Tai, Yu-Wing, Kim, Junmo
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2016
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article Research Support, Non-U.S. Gov't
Beschreibung
Zusammenfassung:In this paper, we introduce a novel approach to automatically detect salient regions in an image. Our approach consists of global and local features, which complement each other to compute a saliency map. The first key idea of our work is to create a saliency map of an image by using a linear combination of colors in a high-dimensional color space. This is based on an observation that salient regions often have distinctive colors compared with backgrounds in human perception, however, human perception is complicated and highly nonlinear. By mapping the low-dimensional red, green, and blue color to a feature vector in a high-dimensional color space, we show that we can composite an accurate saliency map by finding the optimal linear combination of color coefficients in the high-dimensional color space. To further improve the performance of our saliency estimation, our second key idea is to utilize relative location and color contrast between superpixels as features and to resolve the saliency estimation from a trimap via a learning-based algorithm. The additional local features and learning-based algorithm complement the global estimation from the high-dimensional color transform-based algorithm. The experimental results on three benchmark datasets show that our approach is effective in comparison with the previous state-of-the-art saliency estimation methods
Beschreibung:Date Completed 18.03.2016
Date Revised 24.11.2015
published: Print-Electronic
Citation Status PubMed-not-MEDLINE
ISSN:1941-0042
DOI:10.1109/TIP.2015.2495122