A correlation graph approach for unsupervised manifold learning in image retrieval tasks

In this paper, the authors Daniel Pedronette and Ricardo Torres, discuss a novel unsupervised manifold learning algorithm, which aims at imitating the human behavior in judging similarity among images. The proposed algorithm exploits unlabeled contextual information encoded in the dataset manifold through the Correlation Graph for improving the effectiveness of distance/similarity measures. In this sense, the context can be seen as any complementary information about similarity among images, as the set of images in a strongly connected component.

A large set of experiments was conducted for assessing the effectiveness of the proposed approach, considering different descriptors and datasets. The high effectiveness of the manifold learning algorithm is demonstrated by the experimental results obtained in several image retrieval tasks. The effective- ness gains associated with the low computational efforts required represent a significant advantage of the discussed method when compared with existing approaches proposed in the literature.


Daniel Carlos Guimarães Pedronette, Ricardo da S. Torres, A correlation graph approach for unsupervised manifold learning in image retrieval tasks, Neurocomputing, Volume 208, 5 October 2016, Pages 66-79, ISSN 0925-2312, http://dx.doi.org/10.1016/j.neucom.2016.03.081.

Distance correlation analysis for computing the adjacency of the Correlation Graph

Distance correlation analysis for computing the adjacency of the Correlation Graph

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