The Community for Technology Leaders
Green Image
Issue No. 04 - April (2014 vol. 36)
ISSN: 0162-8828
pp: 824-830
Jonathan Masci , Swiss AI Lab. (IDSIA), Univ. of Lugano (USI), Lugano, Switzerland
Michael M. Bronstein , Inst. of Comput. Sci., Univ. of Lugano (USI), Lugano, Switzerland
Alexander M. Bronstein , Sch. of Electr. Eng., Tel Aviv Univ., Tel Aviv, Israel
Jürgen Schmidhuber , Swiss AI Lab. (IDSIA), Univ. of Lugano (USI), Lugano, Switzerland
We introduce an efficient computational framework for hashing data belonging to multiple modalities into a single representation space where they become mutually comparable. The proposed approach is based on a novel coupled siamese neural network architecture and allows unified treatment of intra- and inter-modality similarity learning. Unlike existing cross-modality similarity learning approaches, our hashing functions are not limited to binarized linear projections and can assume arbitrarily complex forms. We show experimentally that our method significantly outperforms state-of-the-art hashing approaches on multimedia retrieval tasks.
Training, Measurement, Vectors, Neural networks, Standards, Optimization, Databases

J. Masci, M. M. Bronstein, A. M. Bronstein and J. Schmidhuber, "Multimodal Similarity-Preserving Hashing," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 36, no. 4, pp. 824-830, 2014.
205 ms
(Ver 3.3 (11022016))