Multi-Modal Deep Learning for Hyperspectral Image Classification

Multi-Modal Deep Learning for Hyperspectral Image Classification – Deep neural networks have recently found ways to outperform traditional methods in image classification tasks. The goal is to understand the underlying problem and formulate it more effectively, rather than learning from large corpora of data. In this paper we propose a novel deep neural network architecture that is capable of extracting the semantic information from large corpora. The proposed network is composed of two layers and one recurrent layer. We first define the semantic information layer as a multi-dimensional multi-level representation network, which is integrated and has a different architecture than that of previous deep architecture. The network learns to recognize objects in a 3D space. The second layer is a recurrent layer which is used to encode the objects’ attributes and the attributes’ weights in a 3D space. The recurrent layers are used for visual information extraction for object classification tasks. Our network achieves a mean accuracy of 95%. Experimental results on the MSU-100M, V1 and PASCAL VOC datasets demonstrate improvements in classification performance.

We present the concept to learn features that outperform a conventional classification algorithm. Our framework is based on a novel method of learning features (with a certain type of information) on images for decoding. This information is extracted from a dictionary of features which include the words and phrases of each word that is used as the basis for classification. The feature extraction is performed on the images of speech given by a human speaker. With this framework, we can build a more advanced classification model which can achieve better performance in most cases. We have evaluated our framework online on some public datasets. The results show a good performance over traditional CNNs, as we have more interpretable features as well as better predictions than the best baselines.

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  • Directional LREW-based 3D shape reconstruction: an overview

    Learning to Predict and Compare Features for Audio ClassificationWe present the concept to learn features that outperform a conventional classification algorithm. Our framework is based on a novel method of learning features (with a certain type of information) on images for decoding. This information is extracted from a dictionary of features which include the words and phrases of each word that is used as the basis for classification. The feature extraction is performed on the images of speech given by a human speaker. With this framework, we can build a more advanced classification model which can achieve better performance in most cases. We have evaluated our framework online on some public datasets. The results show a good performance over traditional CNNs, as we have more interpretable features as well as better predictions than the best baselines.


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