On using bidirectional recurrent neural network to understand image features


On using bidirectional recurrent neural network to understand image features – This paper describes an application of recurrent neural networks (RNN) to semantic analysis of human facial expressions and language. The results show the great potential of the RNN for representing human expressions in a language-independent manner. One of the main benefits of the language-independent RNN is that human expressions can be directly represented in the RNN. More importantly, the human-generated face is automatically recognized and recognized in the RNN. Therefore, we believe that using the RNN as the source of facial expression data is well suited to study face recognition.

While the majority of the methods used for video classification make use of linear features derived from the target sequence, many existing models use a series of feature vectors instead of image features. We propose a novel class of features which is a mixture of linear and nonconvex representations of image labels that is significantly richer in information and is more appropriate for classifying a class of images. The new feature representation can be generalized to any nonlinear or non-convex matrix or is trained as a linear model using the class of image labels as training data. We illustrate how the new representation is used for learning and learning-based classification using both synthetic and real neural networks.

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On using bidirectional recurrent neural network to understand image features

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  • A Semantic Matching Based Algorithm for Multi-Party Conversations: Application to House Orienteering

    Training of Deep Convolutional Neural Networks for Large-Scale Video ClassificationWhile the majority of the methods used for video classification make use of linear features derived from the target sequence, many existing models use a series of feature vectors instead of image features. We propose a novel class of features which is a mixture of linear and nonconvex representations of image labels that is significantly richer in information and is more appropriate for classifying a class of images. The new feature representation can be generalized to any nonlinear or non-convex matrix or is trained as a linear model using the class of image labels as training data. We illustrate how the new representation is used for learning and learning-based classification using both synthetic and real neural networks.


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