A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual attention detection in video sequences using spatiotemporal cues
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Salient region detection and segmentation
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
A biologically inspired computational model for image saliency detection
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Image Annotation by Graph-Based Inference With Integrated Multiple/Single Instance Representations
IEEE Transactions on Multimedia
Robust Spatial Matching for Object Retrieval and Its Parallel Implementation on GPU
IEEE Transactions on Multimedia
Sparse Ensemble Learning for Concept Detection
IEEE Transactions on Multimedia
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We develop a novel algorithm for detecting salient regions. By analyzing the advantages and disadvantages of the existing methods, five principles for designing salient region detection algorithms are summarized. Based on these principles, we propose a novel method that generates saliency map with highlighted salient regions by utilizing two different features, namely visual saliency value and spatial weight. The visual saliency value is determined based on local contrast differences and low-level feature frequencies. The spatial weight is computed by analyzing the size and location of salient regions. Experimental results show that the proposed algorithm outperforms 7 state-of-the-art methods on the public image set.