DocumentCode :
57757
Title :
Coupled Projections for Adaptation of Dictionaries
Author :
Shekhar, Sumit ; Patel, Vishal M. ; Hien Van Nguyen ; Chellappa, Rama
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Maryland´s, College Park, MD, USA
Volume :
24
Issue :
10
fYear :
2015
fDate :
Oct. 2015
Firstpage :
2941
Lastpage :
2954
Abstract :
Data-driven dictionaries have produced the state-of-the-art results in various classification tasks. However, when the target data has a different distribution than the source data, the learned sparse representation may not be optimal. In this paper, we investigate if it is possible to optimally represent both source and target by a common dictionary. In particular, we describe a technique which jointly learns projections of data in the two domains, and a latent dictionary which can succinctly represent both the domains in the projected low-dimensional space. The algorithm is modified to learn a common discriminative dictionary, which can further improve the classification performance. The algorithm is also effective for adaptation across multiple domains and is extensible to nonlinear feature spaces. The proposed approach does not require any explicit correspondences between the source and target domains, and yields good results even when there are only a few labels available in the target domain. We also extend it to unsupervised adaptation in cases where the same feature is extracted across all domains. Further, it can also be used for heterogeneous domain adaptation, where different features are extracted for different domains. Various recognition experiments show that the proposed method performs on par or better than competitive state-of-the-art methods.
Keywords :
feature extraction; image classification; image representation; classification performance; common discriminative dictionary learning; coupled projections; data driven dictionary adaptation; feature extraction; heterogeneous domain adaptation; image classification; image recognition; learned sparse representation; nonlinear feature space; Cost function; Dictionaries; Feature extraction; Joints; Kernel; Manifolds; Dictionary learning; dictionary learning; heterogeneous adaptation; joint projection and dictionary learning; non-linear representation; semi-supervised domain adaptation; shared dictionary; unsupervised adaptation;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2015.2431440
Filename :
7104143
Link To Document :
بازگشت