Bag of features (BoF) representation has attracted an increasing amount of attention in large scale image processing systems. BoF representation treats images as loose collections of local invariant descriptors extrac...
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People are more and more willing to participate in online forums to share their knowledge and experience. However, it may not be easy for them to find their desired threads in online forums due to the information over...
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Traditional level-set-based methods of tracking contours suffered from occlusion and fusion. In this paper, the proposed method introduces dynamic incident detection to find and handle occlusion and fusion. Color hist...
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Traditional level-set-based methods of tracking contours suffered from occlusion and fusion. In this paper, the proposed method introduces dynamic incident detection to find and handle occlusion and fusion. Color histogram of the hue component in HSV color space is used to identify the objects re-entering after occlusion. On the other hand, object features including the size and the motion pattern are utilized to remove the fake regions that are fused with the object region. Besides, a comprehensive foreground extraction (CFE) method based on the combination of background subtraction and Local Binary Pattern (LBP) is proposed. It is fast and robust. Our method is Experiments show that the proposed approach outperforms previous methods on both speed and subjective quality.
Content Based Image Retrieval (CBIR) has become one of the most active research areas in computerscience. Relevance feedback is often used in CBIR systems to bridge the semantic gap. Typically, users are asked to mak...
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ISBN:
(纸本)9781605586083
Content Based Image Retrieval (CBIR) has become one of the most active research areas in computerscience. Relevance feedback is often used in CBIR systems to bridge the semantic gap. Typically, users are asked to make relevance judgements on some query results, and the feedback information is then used to re-rank the images in the database. An effective relevance feedback algorithm must provide the users with the most informative images with respect to the ranking function. In this paper, we propose a novel active learning algorithm, called Convex Laplacian Regularized Ioptimal Design (CLapRID), for relevance feedback image retrieval. Our algorithm is based on a regression model which minimizes the least square error on the labeled images and simultaneously preserves the intrinsic geometrical structure of the image space. It selects the most informative images which minimize the average predictive variance. The optimization problem of CLapRID can be cast as a semidefinite programming (SDP) problem, and solved via interior-point methods. Experimental results on COREL database have demonstrate the effectiveness of the proposed algorithm for relevance feedback image retrieval. Copyright 2009 ACM.
The tradeoff between revenue and market share triggers the emergence of targeted advertising. However, all previous related work only focuses on the topical relevance of ads and does not concern the attitudes of consu...
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The tradeoff between revenue and market share triggers the emergence of targeted advertising. However, all previous related work only focuses on the topical relevance of ads and does not concern the attitudes of consumers. In our paper, we propose a novel advertising strategy DASA (dissatisfaction-oriented advertising based on sentiment analysis) which takes the attitudes of consumers into consideration and promotes ads according to what consumers are unsatisfied with. Our work is most suitable for user generated content which contains plenty of consumerspsila opinionated information. The experiments show encouraging results.
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