DocumentCode :
3426922
Title :
A General Two-Step Approach to Learning-Based Hashing
Author :
Guosheng Lin ; Chunhua Shen ; Suter, David ; van den Hengel, A.
Author_Institution :
Univ. of Adelaide, Adelaide, SA, Australia
fYear :
2013
fDate :
1-8 Dec. 2013
Firstpage :
2552
Lastpage :
2559
Abstract :
Most existing approaches to hashing apply a single form of hash function, and an optimization process which is typically deeply coupled to this specific form. This tight coupling restricts the flexibility of the method to respond to the data, and can result in complex optimization problems that are difficult to solve. Here we propose a flexible yet simple framework that is able to accommodate different types of loss functions and hash functions. This framework allows a number of existing approaches to hashing to be placed in context, and simplifies the development of new problem-specific hashing methods. Our framework decomposes the hashing learning problem into two steps: hash bit learning and hash function learning based on the learned bits. The first step can typically be formulated as binary quadratic problems, and the second step can be accomplished by training standard binary classifiers. Both problems have been extensively studied in the literature. Our extensive experiments demonstrate that the proposed framework is effective, flexible and outperforms the state-of-the-art.
Keywords :
cryptography; learning (artificial intelligence); optimisation; binary quadratic problems; complex optimization problems; general two step approach; hash bit learning; hash function; hash function learning; hashing learning problem; learning based hashing; optimization process; Binary codes; Hamming distance; Kernel; Optimization; Support vector machines; Testing; Training; binary codes; hashing; image retrieval;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision (ICCV), 2013 IEEE International Conference on
Conference_Location :
Sydney, NSW
ISSN :
1550-5499
Type :
conf
DOI :
10.1109/ICCV.2013.317
Filename :
6751428
Link To Document :
بازگشت