Towards CNN-based Image Retrieval with Multi-View Fusion


Towards CNN-based Image Retrieval with Multi-View Fusion – This work is an open-access project of the German University of Frankfurt, which is an extension of the School of Computer Science of the University of Leuven. To the best of our knowledge this is the first work which takes a step towards a deep learning-based image retrieval task using CNN-based neural network models. The idea was previously proposed in this paper as a step towards using network-based classification, which is an extension of the traditional visual retrieval task. To better address the need for deep neural network based CNN-based discriminative representations and for the purpose of training deep models we implemented a neural network model training with Convolutional Neural Networks (CNNs). The training procedure of CNN was to select a CNN to perform attribute analysis for training classifier, then a CNN to generate predictions for attribute. In our experiments we have demonstrated that CNNs have very good performance in classification tasks when using CNNs trained for CNN extraction.

We propose and analyze a framework for automatic segmentation of high-resolution face images by exploiting the temporal and spatial information. Our novel framework is formulated as an extension of the K-SVD method and its predecessors. It consists of a Convolutional Neural Network (CNN), a Convolutional Linear Network (CNNLN), a Convolutional Neural Network (CNN-DNN), Deep Convolutional Neural Network (CNN-DNN), and a Convolutional Neural Network (CNN-RNN). We demonstrate its ability to extract high-resolution face images and segment large-scale images while minimizing the task cost with a small training set size. The CNN is trained end-to-end. Our experimental results show that our approach outperforms the state-of-the-art approaches in terms of segmentation cost while obtaining lower annotations.

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Towards CNN-based Image Retrieval with Multi-View Fusion

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  • Binary Constraint Programming for Big Data and Big Learning

    Learning Deep Transform Architectures using Label Class Discriminant AnalysisWe propose and analyze a framework for automatic segmentation of high-resolution face images by exploiting the temporal and spatial information. Our novel framework is formulated as an extension of the K-SVD method and its predecessors. It consists of a Convolutional Neural Network (CNN), a Convolutional Linear Network (CNNLN), a Convolutional Neural Network (CNN-DNN), Deep Convolutional Neural Network (CNN-DNN), and a Convolutional Neural Network (CNN-RNN). We demonstrate its ability to extract high-resolution face images and segment large-scale images while minimizing the task cost with a small training set size. The CNN is trained end-to-end. Our experimental results show that our approach outperforms the state-of-the-art approaches in terms of segmentation cost while obtaining lower annotations.


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