Constrained Multi-View Image Classification with Multi-temporal Deep CNN Regressions


Constrained Multi-View Image Classification with Multi-temporal Deep CNN Regressions – We propose a novel non-Gaussianized Multi-views (MMTV) approach for high-dimensional object localization. An important limitation in MMTV, for two reasons, is that the non-Gaussianity of the features is not well studied in the non-Gaussianity data. To address this problem we propose a novel multi-view data structure, which is designed to be able to model the structure of non-Gaussian features in the non-Gaussian space. Our framework, termed as MMTV, extends a novel multi-views classifier from the non-Gaussianity learning paradigm, which is a priori and has not been used in the high-dimensional data. Our framework scales to large data sets of multiple subjects, and can be easily extended to other data sets. Furthermore, we prove that the proposed method scales to an unknown low-dimensional space by using the underlying model.

This paper addresses the problem of learning a discriminative image of a person from two labeled images. Existing approaches address this problem by using latent representation learning and latent embedding. However, the underlying latent embedding structure often fails to capture the underlying person identity structure. In this paper, proposed approaches address this problem by learning deep representations of latent spaces. These representations are learned using the image features that have been captured from a shared space, thus providing a more robust discriminative model of the person. Extensive numerical experiments on two publicly available datasets demonstrate the effectiveness of our proposed approach. The results indicate that our approach can be used for person identification tasks in a non-convex problem with high dimensionality.

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Constrained Multi-View Image Classification with Multi-temporal Deep CNN Regressions

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    Video Anomaly Detection Using Learned Convnet FeaturesThis paper addresses the problem of learning a discriminative image of a person from two labeled images. Existing approaches address this problem by using latent representation learning and latent embedding. However, the underlying latent embedding structure often fails to capture the underlying person identity structure. In this paper, proposed approaches address this problem by learning deep representations of latent spaces. These representations are learned using the image features that have been captured from a shared space, thus providing a more robust discriminative model of the person. Extensive numerical experiments on two publicly available datasets demonstrate the effectiveness of our proposed approach. The results indicate that our approach can be used for person identification tasks in a non-convex problem with high dimensionality.


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