A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
2d Object Detection and Recognition: Models, Algorithms, and Networks
2d Object Detection and Recognition: Models, Algorithms, and Networks
Models of bottom-up and top-down visual attention
Models of bottom-up and top-down visual attention
Gaze-based interaction for semi-automatic photo cropping
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A Coherent Computational Approach to Model Bottom-Up Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2006 Papers
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Learning to Detect a Salient Object
IEEE Transactions on Pattern Analysis and Machine Intelligence
Global contrast based salient region detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A Rule Based Technique for Extraction of Visual Attention Regions Based on Real-Time Clustering
IEEE Transactions on Multimedia
An Adaptive Computational Model for Salient Object Detection
IEEE Transactions on Multimedia
Unsupervised extraction of visual attention objects in color images
IEEE Transactions on Circuits and Systems for Video Technology
Saliency and Gist Features for Target Detection in Satellite Images
IEEE Transactions on Image Processing
A unified approach to salient object detection via low rank matrix recovery
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Context-Aware Saliency Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Salient object detection: a benchmark
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
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Despite significant amount of research works, the best available visual attention models still lag far behind human performance in predicting salient object. In this paper, we present a novel approach to detect a salient object which involves two phases. In the first phase, three features such as multi-scale contrast, center-surround histogram and color spatial distribution are obtained as described in Liu et al. model. Constrained Particle Swarm Optimization is used in the second phase to determine an optimal weight vector to combine these features to obtain saliency map to distinguish a salient object from the image background. To achieve this, we defined a simple fitness function which highlights a salient object region with well-defined boundary and effectively suppresses the background regions in an image. The performance is evaluated both qualitatively and quantitatively on a publicly available dataset. Experimental results demonstrate that the proposed model outperforms existing state-of-the-art methods in terms of precision, recall, F -measure and area under curve.