Salient object detection method using random graph

In this paper, a bottom-up salient object detection method is proposed by modeling image as a random graph. The proposed method starts with portioning input image into superpixels and extracting color and spatial features for each superpixel. Then, a complete graph is constructed by employing superp...

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Bibliographic Details
Published in:Multimedia tools and applications 2018-10, Vol.77 (19), p.24681-24699
Main Authors: Nouri, Fatemeh, Kazemi, Kamran, Danyali, Habibollah
Format: Article
Language:English
Subjects:
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Feature extraction
Graph theory
Graphs
Image detection
Image retrieval
Methods
Multimedia Information Systems
Object recognition
Probability
Propagation
Random walk
Salience
Special Purpose and Application-Based Systems
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Summary:In this paper, a bottom-up salient object detection method is proposed by modeling image as a random graph. The proposed method starts with portioning input image into superpixels and extracting color and spatial features for each superpixel. Then, a complete graph is constructed by employing superpixels as nodes. A high edge weight is assigned into a pair of superpixels if they have high similarity. Next, a random walk prior on nodes is assumed to generate the probability distribution on edges. On the other hand, a complete directed graph is created that each edge weight represents the probability for transmitting random walker from current node to next node. By considering a threshold and eliminating edges with higher probability than the threshold, a random graph is created to model input image. The inbound degree vector of a random graph is computed to determine the most salient nodes (regions). Finally, a propagation technique is used to form saliency map. Experimental results on two challenging datasets: MSRA10K and SED2 demonstrate the efficiency of the proposed unsupervised RG method in comparison with the state-of-the-art unsupervised methods.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-018-5668-3