Efficient fusion of multidimensional descriptors for image retrieval
Neelanjan Bhowmik, Ricardo Gonzalez V., Valérie Gouet-Brunet, Hélio Pedrini, Gabriel Bloch · 2014
Due to the large diversity of existing feature descriptors in content-based image retrieval, the image contents can be better represented by the joint use of several descriptors in order to explore their potentially complementary characteristics. This paper presents and discusses a strategy for fusion of the different multidimensional features involved, based on inverted multi-indices and dedicated to similarity search. Image descriptors are quantized separately and efficiently through dimension reduction techniques, before being combined in the inverted multi-indices. To exhibit its effectiveness, the proposal is evaluated on two datasets having different contents and sizes, facing several state-of-the-art approaches of image descriptor fusion. The obtained results reconfirm that the joint use of several descriptions improves similarity search, and show that our fusion proposal outperforms other solutions, while manipulating lower or similar volumes of features.