Unsupervised manifold learning through reciprocal kNN graph and Connected Components for image retrieval tasks. (March 2018)

• Record Type: Journal Article
• Authors: Pedronette, Daniel Carlos Guimarães; Gonçalves, Filipe Marcel Fernandes; Guilherme, Ivan Rizzo
• Is Part Of: Pattern recognition. Volume 75(2018:Mar.)
• Page Start: 161
• Page End: 174
• Publication Date: 2018-03
• Subjects: Content-based image retrieval -- Unsupervised manifold learning -- Reciprocal kNN graph -- Connected components; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.05.009
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Presentation of an unsupervised manifold learning method based on Reciprocal kNN Graphs and Connected Components. Discussion about the use of the method for distance learning in order to improve the effectiveness of image retrieval tasks. Discussion about contributions, algorithm's efficiency and progresses in front of other unsupervised approaches. Experimental evaluation considering various datasets, several features and comparison with state-of-the-art methods. Abstract: Performing effective image retrieval tasks, capable of exploiting the underlying structure of datasets still constitutes a challenge research scenario. This paper proposes a novel manifold learning approach that exploits the intrinsic dataset geometry for improving the effectiveness of image retrieval tasks. The underlying dataset manifold is modeled and analyzed in terms of a Reciprocal kNN Graph and its Connected Components. The method computes the new retrieval results on an unsupervised way, without the need of any user intervention. A large experimental evaluation was conducted, considering different image retrieval tasks, various datasets and features. The proposed method yields better effectiveness results than various methods recently proposed, achieving effectiveness gains up to +40.75%.