A BFS-Tree of ranking references for unsupervised manifold learning. (March 2021) - Pedronette, Daniel Carlos Guimarães
Highlights: A novel unsupervised manifold learning algorithm based on the BFS- Tree of Ranking References. The structure of the tree is exploited to discovery underlying similarity relationships. Based on the discovered relationships, a more effective similarity measure is computed. Several experiments considering various datasets, features and comparison with state-of-the-art methods. Abstract: Contextual information, defined in terms of the proximity of feature vectors in a feature space, has been successfully used in the construction of search services. These search systems aim to exploit such information to effectively improve ranking results, by taking into account the manifold distribution of features usually encoded. In this paper, a novel unsupervised manifold learning is proposed through a similarity representation based on ranking references. A breadth-first tree is used to represent similarity information given by ranking references and is exploited to discovery underlying similarity relationships. As a result, a more effective similarity measure is computed, which leads to more relevant objects in the returned ranked lists of search sessions. Several experiments conducted on eight public datasets, commonly used for image retrieval benchmarking, demonstrated that the proposed method achieves very high effectiveness results, which are comparable or superior to the ones produced by state-of-the-art approaches.