A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dictionary learning algorithms for sparse representation
Neural Computation
Contrast-based image attention analysis by using fuzzy growing
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Sketch2Photo: internet image montage
ACM SIGGRAPH Asia 2009 papers
Salient region detection and segmentation
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Is bottom-up attention useful for object recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Beyond search: Event-driven summarization for web videos
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Bottom-up saliency based on weighted sparse coding residual
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Image Signature: Highlighting Sparse Salient Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual saliency detection by spatially weighted dissimilarity
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Unsupervised extraction of visual attention objects in color images
IEEE Transactions on Circuits and Systems for Video Technology
Saliency Detection by Multitask Sparsity Pursuit
IEEE Transactions on Image Processing
State-of-the-Art in Visual Attention Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The human visual system (HSV) is quite adept at swiftly detecting objects of interest in complex visual scene. Simulating human visual system to detect visually salient regions of an image has been one of the active topics in computer vision. Inspired by random sampling based bagging ensemble learning method, an ensemble dictionary learning (EDL) framework for saliency detection is proposed in this paper. Instead of learning a universal dictionary requiring a large number of training samples to be collected from natural images, multiple over-complete dictionaries are independently learned with a small portion of randomly selected samples from the input image itself, resulting in more flexible multiple sparse representations for each of the image patches. To boost the distinctness of salient patch from background region, we present a reconstruction residual based method for dictionary atom reduction. Meanwhile, with the obtained multiple probabilistic saliency responses for each of the patches, the combination of them is finally carried out from the probabilistic perspective to achieve better predictive performance on saliency region. Experimental results on several open test datasets and some natural images demonstrate that the proposed EDL for saliency detection is much more competitive compared with some existing state-of-the-art algorithms.