Paper Info
Title
Salient Region Detection via Integrating Diffusion-Based Compactness and Local Contrast
Abstract
Salient region detection is a challenging problem and an important topic in computer vision. It has a wide range of applications, such as object recognition and segmentation. Many approaches have been proposed to detect salient regions using different visual cues, such as compactness, uniqueness, and objectness. However, each visual cue-based method has its own limitations. After analyzing the advantages and limitations of different visual cues, we found that compactness and local contrast are complementary to each other. In addition, local contrast can very effectively recover incorrectly suppressed salient regions using compactness cues. Motivated by this, we propose a bottom-up salient region detection method that integrates compactness and local contrast cues. Furthermore, to produce a pixel-accurate saliency map that more uniformly covers the salient objects, we propagate the saliency information using a diffusion process. Our experimental results on four benchmark data sets demonstrate the effectiveness of the proposed method. Our method produces more accurate saliency maps with better precision-recall curve and higher F-Measure than other 19 state-of-the-arts approaches on ASD, CSSD, and ECSSD data sets.
Year
DOI
Venue
2015
10.1109/TIP.2015.2438546
IEEE Trans. Image Processing
Keywords
Field
DocType
Salient region detection, compactness, local contrast, diffusion process, manifold ranking, random walks
Sensory cue,Computer vision,Object detection,Pattern recognition,Salience (neuroscience),Visualization,Segmentation,Compact space,Artificial intelligence,Mathematics,Cognitive neuroscience of visual object recognition,Salient
Journal
Volume
Issue
ISSN
24
11
1057-7149
Citations 
PageRank 
References 
27
0.73
46
Authors
5
Name
Order
Citations
PageRank
li zhou15810.92
Zhaohui Yang2321.45
Qing Yuan3270.73
Zongtan Zhou441233.89
Dewen Hu51290101.20