Salient region detection using discriminative feature selection

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.