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1. IEEE 2014 MATLAB PROJECT
REGION-BASED SALIENCY DETECTION AND ITS APPLICATION IN OBJECT RECOGNITION
ABSTRACT:
The objective of this paper is twofold. First, we introduce an effective region-based
solution for saliency detection. Then, we apply the achieved saliency map to better encode the
image features for solving object recognition task. To find the perceptually and semantically
meaningful salient regions, we extract superpixels based on an adaptive mean shift algorithm as
the basic elements for saliency detection. The saliency of each superpixel is measured by using its
spatial compactness, which is calculated according to the results of Gaussian mixture model
(GMM) clustering. To propagate saliency between similar clusters, we adopt a modified PageRank
algorithm to refine the saliency map. Our method not only improves saliency detection through
large salient region detection and noise tolerance in messy background, but also generates
saliency maps with a well-defined object shape. Experimental results demonstrate the
effectiveness of our method. Since the objects usually correspond to salient regions, and these
regions usually play more important roles for object recognition than background, we apply our
achieved saliency map for object recognition by incorporating a saliency map into sparse coding-
based spatial pyramid matching (ScSPM) image representation. To learn a more discriminative
codebook and better encode the features corresponding to the patches of the objects, we
propose a weighted sparse coding for feature coding. Moreover, we also propose a saliency
weighted max pooling to further emphasize the importance of those salient regions in feature
pooling module. Experimental results on several datasets illustrate that our weighted ScSPM
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2. framework greatly outperforms ScSPM framework, and achieves excellent performance for object
recognition.
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