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Exploiting high level feature for dynamic textures recognition
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DOI：10.1016/j.neucom.2014.12.001.png)
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Abstract
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In this paper, a novel framework is proposed for dynamic textures (DTs) recognition by learning a high level feature using deep neural network (DNN). The insight behind the method is that a DT appearing in different videos should share similar features, which can be learned for better recognition performance. Unlike many prior works only focus on low level or middle level features, we propose a novel high level feature learning method using DNN. Our goal is to construct a compact and discriminative semantic feature. The conventional bag of features approach using k-means is not semantically meaningful since the clustering criterion is based on appearance similarity. The proposed framework can effectively overcome the problem by capturing the semantic relations of the middle level by DNN. Extensive experiments with qualitative and quantitative results demonstrate the efficacy of our approach. (C) 2014 Elsevier B.V. All rights reserved.
Keywords:
Chaotic features
Deep neural network
Bag of features
Dynamic textures recognition
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