In this paper, a novel sparse feature representation method for object tracking is proposed. The method is on the observation that a tracked object can be dynamically and compactly represented by a few features (spars...
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In this paper, a novel sparse feature representation method for object tracking is proposed. The method is on the observation that a tracked object can be dynamically and compactly represented by a few features (sparse representation) from a large feature set (the improved histogram of oriented gradient and color, HOGC). Based on the HOGC features, the sparse representation can be learned online from the constructed training samples during the tracking procedure by exploiting the L1-norm minimization principle, which can also be called feature selection procedure, ensuring the tracking can adapt to the appearance variations of either foreground or background. Experiments with comparisons demonstrate the effectiveness of the proposed method.
An accuracy assessment method that integrates segmentation and classification accuracy is proposed to meet the requirements of object-based image analysis. Segmentation errors are measured by establishing the relation...
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To alleviate the workload of labeling before estimating certain color distributions, integrative labeling is introduced, which merely needs to figure out whether a picture contains positive-class regions or not and th...
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To alleviate the workload of labeling before estimating certain color distributions, integrative labeling is introduced, which merely needs to figure out whether a picture contains positive-class regions or not and then all pixels of the picture are treated as positive or negative class training samples. Integrative labeling, however, results in heavy mixture of training samples. Thus traditional generative density estimation methods can't be used directly in that they perform poorly with heavily polluted training samples. In this paper, by utilizing the prior knowledge of high separability between positive and negative class color distributions, a discriminative learning based GMM(DiscGMM) is proposed for integrative labeling. Besides generating the polluted positive-class samples with comparatively high probability, optimal parameters found by DiscGMM also enjoy a comparatively low probability of generating negative-class samples. The parameter learning problem is solved by a modified Expectation Maximization (EM) algorithm. In an integrative labeling experiment of skin detection, DiscGMM is testified to enjoy much better performance than generative density estimation methods and shows qualified results.
In this paper,we study how to use the search session information to improve the retrieval *** propose a session-oriented retrieval model based on Markov random *** model introduces the correlations between query terms...
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In this paper,we study how to use the search session information to improve the retrieval *** propose a session-oriented retrieval model based on Markov random *** model introduces the correlations between query terms as a retrieval factor into the retrieval *** also presents a dynamic update algorithm based on the analysis of users' search *** model implements a complete session-oriented information retrieval framework *** use ClueWeb09 category B dataset and TREC 2010 (2011) Session dataset to quantitatively evaluate the *** results show that our model can improve retrieval performance substantially using the search session information.
Content-based image retrieval (CBIR) has attracted people's attention for many years,while the semantic gap and curse of dimensionality are still two open questions of *** this paper,we propose a new interactive i...
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Content-based image retrieval (CBIR) has attracted people's attention for many years,while the semantic gap and curse of dimensionality are still two open questions of *** this paper,we propose a new interactive image retrieval method based on locality-sensitive hashing (LSH) and support vector machine (SVM):LSH is adopted to overcome the curse of dimensionality and a SVM-based relevance feedback (RF) scheme is introduced to shorten the semantic *** experimental results show the effectiveness of the proposed method.
This paper proposes a particle swarm optimization(PSO) based particle filter(PF) tracking framework,the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state t...
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This paper proposes a particle swarm optimization(PSO) based particle filter(PF) tracking framework,the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state transition stage,and simultaneously incorporates the newest observations into the proposal distribution in the update *** the proposed approach,likelihood measure functions involving multiple features are presented to enhance the performance of model ***,the multi-feature weights are self-adaptively adjusted by a PSO algorithm throughout the tracking *** are three main ***,the PSO algorithm is fused into the PF framework,which can efficiently alleviate the particles degeneracy ***,an effective convergence criterion for the PSO algorithm is explored,which can avoid particles getting stuck in local minima and maintain a greater particle ***,a multi-feature weight self-adjusting strategy is proposed,which can significantly improve the tracking robustness and *** performed on several challenging public video sequences demonstrate that the proposed tracking approach achieves a considerable performance.
Micro-blog,also known as twitter,is a platform which is based on user relationships for information sharing,spreading and *** how to share the messages in micro-blog efficiently based on the users' interest with a...
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Micro-blog,also known as twitter,is a platform which is based on user relationships for information sharing,spreading and *** how to share the messages in micro-blog efficiently based on the users' interest with analyzing the user information has become a key research *** to the analysis of the micro-blog user relationship and the theory of Analytic Hierarchy Process in the management science,this paper establishes a user influence model,to help explain how the target users affect the core *** the combination of Word Activation Forces Theory,we put forward an algorithm to express the emotional tendentious of each noun-word in all the *** addition,we calculate and find the corresponding tweets to recommend to the core user and the experiment in this paper proves the effectiveness of the algorithm.
In recent years,crowdsourcing has become an effective method in many fields,such as relevance evaluation of search engine ***,the problems of bad workers and quality control always stand among the major challenges wit...
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In recent years,crowdsourcing has become an effective method in many fields,such as relevance evaluation of search engine ***,the problems of bad workers and quality control always stand among the major challenges within the *** on our experiments carried out in TREC 2011 Crowdsourcing track,this paper demonstrates a realtime strategy in recruiting workers and monitoring the quality of their relevance and rank *** effectiveness of our strategy has been verified by empirical results.
Calibration of a functional structural plant model is a challenging task because of the complexity of model structure. Parameter estimation through gradient-based optimization technique was highly dependent on initial...
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This paper develops a reference-based representation method for image categorization and shows that this representation has favorable performance characteristics for multi-class problems. We learn a reconstructive dic...
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