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 welldefined 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 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 Sparse Coding based Spatial Pyramid Matching framework greatly outperforms Sparse Coding based Spatial Pyramid Matching framework, and achieves excellent performance for object recognition.
Multiword expressions (MWEs) have been proved useful for many natural language processing tasks. However, how to use them to improve performance of statistical machine translation (SMT) is not well studied. This paper presents a simple yet effective strategy to extract domain bilingual multiword expressions. In addition, we implement three methods to integrate bilingual MWEs to Moses, the state-ofthe-art phrase-based machine translation system. Experiments show that bilingual MWEs could improve translation performance significantly.
Saliency detection is useful for high level applications such as adaptive compression, image retargeting, object recognition, etc. In this paper, we introduce an effective region-based solution for saliency detection. We first use the adaptive mean shift algorithm to extract superpixels from the input image, then apply Gaussian Mixture Model (GMM) to cluster superpixels based on their color similarity, and finally calculate the saliency value for each cluster using compactness metric together with modified PageRank propagation. This solution is able to represent the image in a perceptually meaningful way and is robust to over-segmentation. It highlights salient regions with full resolution, well-defined boundary. Experimental results show that both the adaptive mean shift and the modified PageRank algorithm contribute substantially to the saliency detection result. In addition, the ROC analysis demonstrates that our approach significantly outperforms five existing popular methods.
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