Salient region detection using discriminative feature selection

Authors:
HyunCheol Kim;Whoi-Yul Kim
Affiliations:
Department of Electronics and Computer Engineering, Hanyang University, Seoul, Republic of Korea;Department of Electronics and Computer Engineering, Hanyang University, Seoul, Republic of Korea
Venue:
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Year:
2011

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Abstract

Detecting visually salient regions is useful for applications such as object recognition/segmentation, image compression, and image retrieval. In this paper we propose a novel method based on discriminative feature selection to detect salient regions in natural images. To accomplish this, salient region detection was formulated as a binary labeling problem, where the features that best distinguish a salient region from its surrounding background are empirically evaluated and selected based on a two-class variance ratio. A large image data set was employed to compare the proposed method to six state-of-the-art methods. From the experimental results, it has been confirmed that the proposed method outperforms the six algorithms by achieving higher precision and better F-measurements.