IEEE Trans Image Process - Representing and retrieving video shots in human-centric brain imaging space.

Tópicos

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Resumo

Meaningful representation and effective retrieval of video shots in a large-scale database has been a profound challenge for the image/video processing and computer vision communities. A great deal of effort has been devoted to the extraction of low-level visual features, such as color, shape, texture, and motion for characterizing and retrieving video shots. However, the accuracy of these feature descriptors is still far from satisfaction due to the well-known semantic gap. In order to alleviate the problem, this paper investigates a novel methodology of representing and retrieving video shots using human-centric high-level features derived in brain imaging space (BIS) where brain responses to natural stimulus of video watching can be explored and interpreted. At first, our recently developed dense individualized and common connectivity-based cortical landmarks (DICCCOL) system is employed to locate large-scale functional brain networks and their regions of interests (ROIs) that are involved in the comprehension of video stimulus. Then, functional connectivities between various functional ROI pairs are utilized as BIS features to characterize the brain's comprehension of video semantics. Then an effective feature selection procedure is applied to learn the most relevant features while removing redundancy, which results in the formation of the final BIS features. Afterwards, a mapping from low-level visual features to high-level semantic features in the BIS is built via the Gaussian process regression (GPR) algorithm, and a manifold structure is then inferred, in which video key frames are represented by the mapped feature vectors in the BIS. Finally, the manifold-ranking algorithm concerning the relationship among all data is applied to measure the similarity between key frames of video shots. Experimental results on the TRECVID 2005 dataset demonstrate the superiority of the proposed work in comparison with traditional methods.

Resumo Limpo

meaning represent effect retriev video shot largescal databas profound challeng imagevideo process comput vision communiti great deal effort devot extract lowlevel visual featur color shape textur motion character retriev video shot howev accuraci featur descriptor still far satisfact due wellknown semant gap order allevi problem paper investig novel methodolog repres retriev video shot use humancentr highlevel featur deriv brain imag space bis brain respons natur stimulus video watch can explor interpret first recent develop dens individu common connectivitybas cortic landmark dicccol system employ locat largescal function brain network region interest roi involv comprehens video stimulus function connect various function roi pair util bis featur character brain comprehens video semant effect featur select procedur appli learn relev featur remov redund result format final bis featur afterward map lowlevel visual featur highlevel semant featur bis built via gaussian process regress gpr algorithm manifold structur infer video key frame repres map featur vector bis final manifoldrank algorithm concern relationship among data appli measur similar key frame video shot experiment result trecvid dataset demonstr superior propos work comparison tradit method

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