Discriminative Semantic Subspace Analysis for Relevance Feedback

Discriminative Semantic Subspace Analysis for Relevance Feedback

Abstract

Content-based image retrieval (CBIR) has attracted much attention during the past decades for its potential practical applications to image database management. A variety of relevance feedback (RF) schemes have been designed to bridge the gap between low-level visual features and high-level semantic concepts for an image retrieval task. In the process of RF, it would be impractical or too expensive to provide explicit class label information for each image. Instead, similar or dissimilar pairwise constraints between two images can be acquired more easily. However, most of the conventional RF approaches can only deal with training images with explicit class label information. In this paper, we propose a novel discriminative semantic subspace analysis (DSSA) method, which can directly learn a semantic subspace from similar and dissimilar pairwise constraints without using any explicit class label information. In particular, DSSA can effectively integrate the local geometry of labeled similar images, the discriminative information between labeled similar and dissimilar images, and the local geometry of labeled and unlabeled images together to learn a reliable subspace. Compared with the popular distance metric analysis approaches, our method can also learn a distance metric but perform more effectively when dealing with high-dimensional images. Extensive experiments on both the synthetic data sets and a real-world image database demonstrate the effectiveness of the proposed scheme in improving the performance of the CBIR.

Publication

Lining Zhang, Hubert P. H. Shum and Ling Shao,
"Discriminative Semantic Subspace Analysis for Relevance Feedback",
IEEE Transactions on Image Processing (TIP)

Citation=19## IF=6.790#

# Impact factor from the Journal Citation Reports as of 2017
## Citation count from Google Scholar as of 2018

Downloads

YouTube

References

BibTeX

@article{zhang16discriminative,
 author={Zhang, Lining and Shum, Hubert P. H. and Shao, Ling},
 journal={IEEE Transactions on Image Processing},
 title={Discriminative Semantic Subspace Analysis for Relevance Feedback},
 year={2016},
 month={Mar},
 volume={25},
 number={3},
 pages={1275--1287},
 numpages={13},
 doi={10.1109/TIP.2016.2516947},
 issn={1057-7149},
 publisher={IEEE},
}

EndNote/RefMan

TY  - JOUR
AU  - Zhang, Lining
AU  - Shum, Hubert P. H.
AU  - Shao, Ling
T2  - IEEE Transactions on Image Processing
TI  - Discriminative Semantic Subspace Analysis for Relevance Feedback
PY  - 2016
Y1  - Mar 2016
VL  - 25
IS  - 3
SP  - 1275
EP  - 1287
DO  - 10.1109/TIP.2016.2516947
SN  - 1057-7149
PB  - IEEE
ER  - 

Plain Text

Lining Zhang, Hubert P. H. Shum and Ling Shao, "Discriminative Semantic Subspace Analysis for Relevance Feedback," IEEE Transactions on Image Processing, vol. 25, no. 3, pp. 1275-1287, IEEE, Mar 2016.

Similar Research

 

         

Last update: 16/09/2019