• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Learning Free Energy Kernel for Image Retrieval

Vol. 8, No. 8, August 28, 2014
10.3837/tiis.2014.08.019, Download Paper (Free):

Abstract

Content-based image retrieval has been the most important technique for managing huge amount of images. The fundamental yet highly challenging problem in this field is how to measure the content-level similarity based on the low-level image features. The primary difficulties lie in the great variance within images, e.g. background, illumination, viewpoint and pose. Intuitively, an ideal similarity measure should be able to adapt the data distribution, discover and highlight the content-level information, and be robust to those variances. Motivated by these observations, we in this paper propose a probabilistic similarity learning approach. We first model the distribution of low-level image features and derive the free energy kernel (FEK), i.e., similarity measure, based on the distribution. Then, we propose a learning approach for the derived kernel, under the criterion that the kernel outputs high similarity for those images sharing the same class labels and output low similarity for those without the same label. The advantages of the proposed approach, in comparison with previous approaches, are threefold. (1) With the ability inherited from probabilistic models, the similarity measure can well adapt to data distribution. (2) Benefitting from the content-level hidden variables within the probabilistic models, the similarity measure is able to capture content-level cues. (3) It fully exploits class label in the supervised learning procedure. The proposed approach is extensively evaluated on two well-known databases. It achieves highly competitive performance on most experiments, which validates its advantages.


Statistics

Show / Hide Statistics

Statistics (Cumulative Counts from December 1st, 2015)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article

[IEEE Style]
C. Wang, B. Wang and L. Zheng, "Learning Free Energy Kernel for Image Retrieval," KSII Transactions on Internet and Information Systems, vol. 8, no. 8, pp. 2895-2912, 2014. DOI: 10.3837/tiis.2014.08.019.

[ACM Style]
Cungang Wang, Bin Wang, and Liping Zheng. 2014. Learning Free Energy Kernel for Image Retrieval. KSII Transactions on Internet and Information Systems, 8, 8, (2014), 2895-2912. DOI: 10.3837/tiis.2014.08.019.