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Discrete Hashing With Multiple Supervision
IEEE Transactions on Image Processing, 2019Supervised hashing methods have achieved more promising results than unsupervised ones by leveraging label information to generate compact and accurate hash codes. Most of the prior supervised hashing methods construct an n × n instance-pairwise similarity matrix, where n is the number of training samples.
Xin Luo +4 more
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Computer Vision and Image Understanding, 2017
Fast nearest neighbor search is becoming more and more crucial given the advent of large-scale data in many computer vision applications. Hashing approaches provide both fast search mechanisms and compact index structures to address this critical need.
Fatih Cakir +2 more
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Fast nearest neighbor search is becoming more and more crucial given the advent of large-scale data in many computer vision applications. Hashing approaches provide both fast search mechanisms and compact index structures to address this critical need.
Fatih Cakir +2 more
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2015 IEEE International Conference on Image Processing (ICIP), 2015
Fast similarity search is becoming more and more critical given the ever growing sizes of datasets. Hashing approaches provide both fast search mechanisms and compact indexing structures to address this critical need. In image retrieval problems where labeled training data is available, supervised hashing methods prevail over un-supervised methods ...
Fatih Cakir, Stan Sclaroff
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Fast similarity search is becoming more and more critical given the ever growing sizes of datasets. Hashing approaches provide both fast search mechanisms and compact indexing structures to address this critical need. In image retrieval problems where labeled training data is available, supervised hashing methods prevail over un-supervised methods ...
Fatih Cakir, Stan Sclaroff
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2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
Among learning based hashing methods, supervised hashing seeks compact binary representation of the training data to preserve semantic similarities. Recent years have witnessed various problem formulations and optimization methods for supervised hashing.
Zihao Hu +3 more
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Among learning based hashing methods, supervised hashing seeks compact binary representation of the training data to preserve semantic similarities. Recent years have witnessed various problem formulations and optimization methods for supervised hashing.
Zihao Hu +3 more
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CapsNet-based supervised hashing
Applied Intelligence, 2021With the development of Internet technology, an increasing amount of data enters people’s daily life, which brings great challenges when users quickly search for interesting images. The existing exact nearest neighbor retrieval methods often fail to obtain results within an acceptable retrieval time, so researchers have begun to focus on approximate ...
Bolin Zhang +4 more
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Semi-supervised online hashing
2016 International Conference on Machine Learning and Cybernetics (ICMLC), 2016Most of existing hashing methods for image retrieval problems assume all images are given at the beginning. However, in some image retrieval problems, images may arrive or be labeled in an online or streaming manner. Current online hashing methods are fully supervised which assume all images come with labels.
Tian Xing, Wing W.Y. Ng
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Semi-supervised Discriminant Hashing
2011 IEEE 11th International Conference on Data Mining, 2011Hashing refers to methods for embedding high dimensional data into a similarity-preserving low-dimensional Hamming space such that similar objects are indexed by binary codes whose Hamming distances are small. Learning hash functions from data has recently been recognized as a promising approach to approximate nearest neighbor search for high ...
Saehoon Kim, Seungjin Choi
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Supervised hashing with kernels
2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012Recent years have witnessed the growing popularity of hashing in large-scale vision problems. It has been shown that the hashing quality could be boosted by leveraging supervised information into hash function learning. However, the existing supervised methods either lack adequate performance or often incur cumbersome model training.
null Wei Liu +4 more
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2016
Hashing methods on large scale image retrieval have been extensively in attention. These methods can be roughly categorized as supervised and unsupervised. Unsupervised hashing methods mainly search for a projection matrix of the original data to preserve the Euclidean distance similarity, while supervised hashing methods aim to preserve the label ...
Tongtong Yuan, Weihong Deng
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Hashing methods on large scale image retrieval have been extensively in attention. These methods can be roughly categorized as supervised and unsupervised. Unsupervised hashing methods mainly search for a projection matrix of the original data to preserve the Euclidean distance similarity, while supervised hashing methods aim to preserve the label ...
Tongtong Yuan, Weihong Deng
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Supervised topology preserving hashing
2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), 2015Learning based hashing is gaining traction in large-scale retrieval systems. It aims to learn compact binary codes that can preserve semantic similarity in the hamming space. This paper presents a supervised topology hashing (SPTH) algorithm to learn compact binary codes that can exploit both the supervisory information as well as the local topology ...
Shu Zhang +4 more
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