Results 11 to 20 of about 26,920 (307)
Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval [PDF]
, 2015 With the rapid growth of web images, hashing has received increasing interests in large scale image retrieval. Research efforts have been devoted to learning compact binary codes that preserve semantic similarity based on labels.
Huang, Yongzhen +3 more
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A Classification Retrieval Method for Encrypted Speech Based on Deep Neural Network and Deep Hashing [PDF]
IEEE Access, 2020 In order to improve the retrieval efficiency and accuracy of the existing encrypted speech retrieval methods, and improve the semantic representation of speech features and classification performance, a classification retrieval method for encrypted ...
Qiuyu Zhang, Xuejiao Zhao, Yingjie Hu
doaj +2 more sources
, 2016 Hashing aims at generating highly compact similarity preserving code words which are well suited for large-scale image retrieval tasks. Most existing hashing methods first encode the images as a vector of hand-crafted features followed by a separate binarization step to generate hash codes. This two-stage process may produce sub-optimal encoding.
Conjeti, Sailesh +3 more
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HHF: Hashing-Guided Hinge Function for Deep Hashing Retrieval [PDF]
IEEE Transactions on Multimedia, 2023 Deep hashing has shown promising performance in large-scale image retrieval. However, latent codes extracted by Deep Neural Networks (DNNs) will inevitably lose semantic information during the binarization process, which damages the retrieval accuracy and makes it challenging.
Chengyin Xu +6 more
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Scalable and Sustainable Deep Learning via Randomized Hashing [PDF]
, 2016 Current deep learning architectures are growing larger in order to learn from complex datasets. These architectures require giant matrix multiplication operations to train millions of parameters.
Chen Wenlin +8 more
core +2 more sources
Deep Feature-Based Neighbor Similarity Hashing With Adversarial Learning for Cross-Modal Retrieval
IEEE AccessCurrently, deep hashing methods for cross-modal retrieval have achieved significant performance. However, label-based pairwise semantic keep correspondence within bounds of tags, while overlooking the connection between the essence of content.
Kun Li +4 more
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Deep Cross-Modal Hashing [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 Due to its low storage cost and fast query speed, cross-modal hashing (CMH) has been widely used for similarity search in multimedia retrieval applications. However, almost all existing CMH methods are based on hand-crafted features which might not be optimally compatible with the hash-code learning procedure.
Jiang, Qing-Yuan, Li, Wu-Jun
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Deep Supervised Discrete Hashing [PDF]
, 2017 With the rapid growth of image and video data on the web, hashing has been extensively studied for image or video search in recent years. Benefit from recent advances in deep learning, deep hashing methods have achieved promising results for image retrieval.
Li, Qi +3 more
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Deep Supervised Hashing for Fast Multi-Label Image
MATEC Web of Conferences, 2018 In this paper, most of the existing Hashing methods is mapping the hand extracted features to binary code, and designing the loss function with the label of images.
Ying Qian, Qingqing Ye
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Dual Asymmetric Deep Hashing Learning [PDF]
IEEE Access, 2019 Due to the impressive learning power, deep learning has achieved a remarkable performance in supervised hash function learning. In this paper, we propose a novel asymmetric supervised deep hashing method to preserve the semantic structure among different
Jinxing Li +3 more
doaj +2 more sources