Effective deep learning-based multi-modal retrieval

Effective deep learning-based multi-modal retrieval Multi-modal retrieval is emerging as a new search paradigm that enables seamless information retrieval from various types of media. For example, users can simply snap a movie poster to search for relevant reviews and trailers. The mainstream solution to the problem is to learn a set of mapping functions that project data from different modalities into a common metric space in which conventional indexing schemes for high-dimensional space can be applied. Since the effectiveness of the mapping functions plays an essential role in improving search quality, in this paper, we exploit deep learning techniques to learn effective mapping functions. In particular, we first propose a general learning objective that effectively captures both intramodal and intermodal semantic relationships of data from heterogeneous sources. Given the general objective, we propose two learning algorithms to realize it: (1) an unsupervised approach that uses stacked auto-encoders and requires minimum prior knowledge on the training data and (2) a supervised approach using deep convolutional neural network and neural language model. Our training algorithms are memory efficient with respect to the data volume. Given a large training dataset, we split it into mini-batches and adjust the mapping functions continuously for each batch. Experimental results on three real datasets demonstrate that our proposed methods achieve significant improvement in search accuracy over the state-of-the-art solutions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Effective deep learning-based multi-modal retrieval

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-015-0391-4
Publisher site
See Article on Publisher Site

Abstract

Multi-modal retrieval is emerging as a new search paradigm that enables seamless information retrieval from various types of media. For example, users can simply snap a movie poster to search for relevant reviews and trailers. The mainstream solution to the problem is to learn a set of mapping functions that project data from different modalities into a common metric space in which conventional indexing schemes for high-dimensional space can be applied. Since the effectiveness of the mapping functions plays an essential role in improving search quality, in this paper, we exploit deep learning techniques to learn effective mapping functions. In particular, we first propose a general learning objective that effectively captures both intramodal and intermodal semantic relationships of data from heterogeneous sources. Given the general objective, we propose two learning algorithms to realize it: (1) an unsupervised approach that uses stacked auto-encoders and requires minimum prior knowledge on the training data and (2) a supervised approach using deep convolutional neural network and neural language model. Our training algorithms are memory efficient with respect to the data volume. Given a large training dataset, we split it into mini-batches and adjust the mapping functions continuously for each batch. Experimental results on three real datasets demonstrate that our proposed methods achieve significant improvement in search accuracy over the state-of-the-art solutions.

Journal

The VLDB JournalSpringer Journals

Published: Feb 1, 2016

References

  • A neural probabilistic language model
    Bengio, Y; Ducharme, R; Vincent, P; Janvin, C
  • Representation learning: a review and new perspectives
    Bengio, Y; Courville, AC; Vincent, P

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