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Deep Supervised Hashing with Information Loss
2018Recently, deep neural networks based hashing methods have greatly improved the image retrieval performance by simultaneously learning feature representations and binary hash functions. Most deep hashing methods utilize supervision information from semantic labels to preserve the distance similarity within local structures, however, the global ...
Xueni Zhang +3 more
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State Abstraction via Deep Supervised Hash Learning
IEEE Transactions on Neural Networks and Learning SystemsState abstraction is a widely used technique in reinforcement learning (RL) that compresses the state space to accelerate learning algorithms. However, designing an effective abstraction function in large-scale or high-dimensional state space problems remains a significant challenge.
Guang Yang +6 more
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Deep Supervised Hashing with Spherical Embedding
2019Deep hashing approaches are widely applied to approximate nearest neighbor search for large-scale image retrieval. We propose Spherical Deep Supervised Hashing (SDSH), a new supervised deep hashing approach to learn compact binary codes. The goal of SDSH is to go beyond learning similarity preserving codes, by encouraging them to also be balanced and ...
Stanislav Pidhorskyi +4 more
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CapsHash: Deep Supervised Hashing with Capsule Network
2019 11th International Conference on Wireless Communications and Signal Processing (WCSP), 2019To better deal with large-scale image retrieval problem, deep hashing models based on convolutional neural network (CNN) have been widely used as effective methods, which can map similar images to compact binary hash codes with smaller hamming distance.
Yang Li +3 more
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Supervised deep hashing for image content security
Multimedia Tools and Applications, 2017Due to the fast growth of image data on the web, it is necessary to ensure the content security of uploaded images. One of the fundamental problems behind this need is retrieving relevant images from the large-scale databases. Recently, hashing/binary coding algorithms have proved to be effective for large-scale visual information retrieval.
Yanping Ma +3 more
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Deep High-order Supervised Hashing for Image Retrieval
2018 24th International Conference on Pattern Recognition (ICPR), 2018Recently, deep hashing has achieved excellent performances in large-scale image retrieval by simultaneously learning deep features and hashing function. However, state-of-the-art works have so far failed to explore the feature statistics higher than first-order.
Jingdong Cheng +4 more
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Supervised deep hashing for scalable face image retrieval
Pattern Recognition, 2018Abstract Hashing has been widely utilized for Approximate Nearest Neighbor (ANN) search due to its fast retrieval speed and low storage cost. In this work, we propose a novel supervised hashing method for scalable face image retrieval, i.e., Deep Hashing based on Classification and Quantization errors (DHCQ), by simultaneously learning feature ...
Jinhui Tang, Zechao Li, Xiang Zhu
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Teach to Hash: A Deep Supervised Hashing Framework with Data Selection
2018Recent years have witnessed wide applications of deep learning for large-scale image hashing tasks, as deep hashing algorithms can simultaneously learn feature representations and hash codes in an end-to-end way. However, although these methods have obtained promising results to some extent, they seldom take the effect of different training samples ...
Xiang Li, Chao Ma, Jie Yang, Yu Qiao
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Deep Supervised Auto-encoder Hashing for Image Retrieval
2018Image hashing approaches map high dimensional images to compact binary codes that preserve similarities among images. Although the image label is important information for supervised image hashing methods to generate hashing codes, the retrieval performance will be limited according to the performance of the classifier.
Sanli Tang +4 more
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