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 An Adaptive Semisupervised Feature Analysis for Video Semantic Recognition
 [期刊] , 2018, 48(2): 648660 SCIE SCOPUS
 被引用 11 (Web of Science℠)

摘要Video semantic recognition usually suffers from the curse of dimensionality and the absence of enough highquality labeled instances, thus semisupervised feature selection gains increasing attentions for its efficiency and comprehensibility. Most of the previous methods assume that videos with close distance (neighbors) have similar labels and characterize the intrinsic local structure through a predetermined graph of both labeled and unlabeled data. However, besides the parameter tuning problem underlying the construction of the graph, the affinity measurement in the original feature space usually suffers from the curse of dimensionality. Additionally, the predetermined graph separates itself from the procedure of feature selection, which might lead to downgraded performance for video semantic recognition. In this paper, we exploit a novel semisupervised feature selection method from a new perspective. The primary assumption underlying our model is that the instances with similar labels should have a larger probability of being neighbors. Instead of using a predetermined similarity graph, we incorporate the exploration of the local structure into the procedure of joint feature selection so as to learn the optimal graph simultaneously. Moreover, an adaptive loss function is exploited to measure the label fitness, which significantly enhances model's robustness to videos with a small or substantial loss. We propose an efficient alternating optimization algorithm to solve the proposed challenging problem, together with analyses on its convergence and computational complexity in theory. Finally, extensive experimental results on benchmark datasets illustrate the effectiveness and superiority of the proposed approach on video semantic recognition related tasks.关键词semisupervised learning , video semantic recognition , Feature selection , manifold regularization
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 Simple to complex crossmodal learning to rank
 [期刊] , 2017, 163(): 6777 EI SCOPUS SCIE
 被引用 3 (Web of Science℠)

摘要© 2017 Elsevier Inc. The heterogeneitygap between different modalities brings a significant challenge to multimedia information retrieval. Some studies formalize the crossmodal retrieval tasks as a ranking problem and learn a shared multimodal embedding space to measure the crossmodality similarity. However, previous methods often establish the shared embedding space based on linear mapping functions which might not be sophisticated enough to reveal more complicated intermodal correspondences. Additionally, current studies assume that the rankings are of equal importance, and thus all rankings are used simultaneously, or a small number of rankings are selected randomly to train the embedding space at each iteration. Such strategies, however, always suffer from outliers as well as reduced generalization capability due to their lack of insightful understanding of procedure of human cognition. In this paper, we involve the selfpaced learning theory with diversity into the crossmodal learning to rank and learn an optimal multimodal embedding space based on nonlinear mapping functions. This strategy enhances the model's robustness to outliers and achieves better generalization via training the model gradually from easy rankings by diverse queries to more complex ones. An efficient alternative algorithm is exploited to solve the proposed challenging problem with fast convergence in practice. Extensive experimental results on several benchmark datasets indicate that the proposed method achieves significant improvements over the stateofthearts in this literature.关键词Crossmodal retrieval  Diversity regularization  Learning to rank  Selfpaced learning
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