Regional Principal Color Based Saliency Detection
Jing Lou,  Mingwu Ren,  Huan Wang
Nanjing University of Science and Technology
Regional Principal Color Based Saliency Detection - Figure 1

Figure 1. Saliency maps vs. ground truth. Given several original images [20] (top), our saliency detection method is used to generate saliency maps by measuring regional principal color contrasts (middle), which are comparable to manually labeled ground truth [11] (bottom).
Abstract

Saliency detection is widely used in many visual applications like image segmentation, object recognition and classification. In this paper, we will introduce a new method to detect salient objects in natural images. The approach is based on a regional principal color contrast modal, which incorporates low-level and medium-level visual cues. The method allows a simple computation of color features and two categories of spatial relationships to a saliency map, achieving higher F-measure rates. At the same time, we present an interpolation approach to evaluate resulting curves, and analyze parameters selection. Our method enables the effective computation of arbitrary resolution images. Experimental results on a saliency database show that our approach produces high quality saliency maps and performs favorably against ten saliency detection algorithms.

Paper
Results
Regional Principal Color Based Saliency Detection - Figure 7

Figure 7. Visual results of our method compared with ground truth and other methods on dataset MSRA-1000. (A) Original images [20]. (B) Ground truth [11]. (C) IT [1]. (D) SR [14]. (E) FT [11]. (F) CA [19]. (G) RC [10]. (H) Ours.
Regional Principal Color Based Saliency Detection - Figure 8

Figure 8. Quantitative comparison on dataset MSRA-1000 (N/A represents no center-bias). (A) Precision-Recall curves. (B) F-measure curves. (C) Precision-Recall bars.
Funding

This research was supported by the National Natural Science Foundation of China (NSFC, Grant nos. 61231014, 60875010).

Acknowledgments

The authors would like to thank all the anonymous reviewers for the constructive comments and useful suggestions that led to improvements in the quality and presentation of this paper. We thank Shuan Wang, Xiang Li, Longtao Chen, and Boyuan Feng for useful discussions. We also thank James S. Krugh and Rui Guo for their kind proofreading of this manuscript.

References
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Latest update:  Aug 27, 2016