Bio-inspired visual saliency detection and its application on image retargeting
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Visual saliency detection with center shift
Neurocomputing
A New Framework for Multiscale Saliency Detection Based on Image Patches
Neural Processing Letters
Oscillation analysis for salient object detection
Multimedia Tools and Applications
Ensemble dictionary learning for saliency detection
Image and Vision Computing
Color boosted visual saliency detection and its application to image classification
Multimedia Tools and Applications
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In this paper, a new visual saliency detection method is proposed based on the spatially weighted dissimilarity. We measured the saliency by integrating three elements as follows: the dissimilarities between image patches, which were evaluated in the reduced dimensional space, the spatial distance between image patches and the central bias. The dissimilarities were inversely weighted based on the corresponding spatial distance. A weighting mechanism, indicating a bias for human fixations to the center of the image, was employed. The principal component analysis (PCA) was the dimension reducing method used in our system. We extracted the principal components (PCs) by sampling the patches from the current image. Our method was compared with four saliency detection approaches using three image datasets. Experimental results show that our method outperforms current state-of-the-art methods on predicting human fixations.