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BrinjalFruitX: A field-collected image dataset for machine learning and deep learning-based disease identification in brinjal fruits. [PDF]
Data BriefBitto AK +6 more
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Entropy guided multi level feature fusion network for high precision content based image retrieval. [PDF]
Sci RepLavanya M +3 more
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Transductive Semisupervised Deep Hashing
IEEE Transactions on Neural Networks and Learning Systems, 2022Deep hashing methods have shown their superiority to traditional ones. However, they usually require a large amount of labeled training data for achieving high retrieval accuracies. We propose a novel transductive semisupervised deep hashing (TSSDH) method which is effective to train deep convolutional neural network (DCNN) models with both labeled and
Weiwei Shi +3 more
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IEEE Transactions on Multimedia, 2017
In this work, we propose a deep video hashing (DVH) method for scalable video search. Unlike most existing video hashing methods that first extract features for each single frame and then use conventional image hashing techniques, our DVH learns binary codes for the entire video with a deep learning framework so that both the temporal and ...
Venice Erin Liong +3 more
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In this work, we propose a deep video hashing (DVH) method for scalable video search. Unlike most existing video hashing methods that first extract features for each single frame and then use conventional image hashing techniques, our DVH learns binary codes for the entire video with a deep learning framework so that both the temporal and ...
Venice Erin Liong +3 more
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ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
Hashing has been widely used for large-scale approximate nearest neighbors retrieval own to its high efficiency. In the existing hashing methods, deep supervised hashing methods have achieved the best performance by utilizing the semantic labels on data with deep learning.
Quan Zhou +4 more
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Hashing has been widely used for large-scale approximate nearest neighbors retrieval own to its high efficiency. In the existing hashing methods, deep supervised hashing methods have achieved the best performance by utilizing the semantic labels on data with deep learning.
Quan Zhou +4 more
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Deep hashing learning networks
2016 International Joint Conference on Neural Networks (IJCNN), 2016Hashing-based methods seek compact and efficient binary codes that preserve the similarity between data. For most existing hashing methods, an input (e.g. image) is first encoded as a vector of hand-crafted visual feature, followed by a hash projection and quantization step to obtain the compact binary vector.
Guoqiang Zhong +4 more
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Asymmetric Deep Hashing for Efficient Hash Code Compression
Proceedings of the 28th ACM International Conference on Multimedia, 2020Benefiting from recent advances in deep learning, deep hashing methods have achieved promising performance in large-scale image retrieval. To improve storage and computational efficiency, existing hash codes need to be compressed accordingly. However, previous deep hashing methods have to retrain their models and then regenerate the whole database ...
Shu Zhao +5 more
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2017 IEEE Winter Conference on Applications of Computer Vision (WACV), 2017
In applications involving matching of image sets, the information from multiple images must be effectively exploited to represent each set. State-of-the-art methods use probabilistic distribution or subspace to model a set and use specific distance measure to compare two sets.
Jie Feng, Svebor Karaman, Shih-Fu Chang
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In applications involving matching of image sets, the information from multiple images must be effectively exploited to represent each set. State-of-the-art methods use probabilistic distribution or subspace to model a set and use specific distance measure to compare two sets.
Jie Feng, Svebor Karaman, Shih-Fu Chang
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Deep Asymmetric Pairwise Hashing
Proceedings of the 25th ACM international conference on Multimedia, 2017Recently, deep neural networks based hashing methods have greatly improved the multimedia retrieval performance by simultaneously learning feature representations and binary hash functions. Inspired by the latest advance in the asymmetric hashing scheme, in this work, we propose a novel Deep Asymmetric Pairwise Hashing approach (DAPH) for supervised ...
Fumin Shen +4 more
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Deep Semantic Asymmetric Hashing
2019Deep hashing, which combines binary codes learning and convolutional neural network, has achieved promising performance for highly efficient image retrieval. Asymmetric deep hashing methods, which treat query points and database points in an asymmetric way perform better than symmetric deep hashing methods on retrieval tasks in both time complexity and
Mian Zhang, Cheng Cheng, Xianzhong Long
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