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Principal Component Analysis-Based Visual Saliency Detection
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DOI：10.1109/TBC.2016.2617291.png)
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Abstract
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In this paper, a novel patch-wise saliency detection algorithm is proposed based on principal component analysis (PCA). As a powerful statistical procedure in data analysis, PCA is fully exploited to convert color space and produce compact patch representation. Specifically, images are first converted to linearly uncorrelated channels and divided into non-overlapped patches. Then the patches are represented by the coefficients of principal components using PCA analysis. Based on the compact representation of patches, two types of distinctiveness are introduced: 1) center-surround contrast and 2) global rarity. Experimental results demonstrate that the PCA-based color space conversion and patch representation can improve the accuracy of human fixations prediction. And the proposed algorithm outperforms the mainstream algorithms on predicting human fixations. Additional experiments on salient object detection and image retargeting show that the proposed model can achieve better performance than traditional models.
Keywords:
Saliency detection
principal component analysis (PCA)
center-surround
rarity
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