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Erschienen in:
A Hybrid Scatter Search Algorithm to Solve the Capacitated Arc Routing Problem with Refill Points Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Deep Learning and Shared Representation Space Learning Based Cross-Modal Multimedia Retrieval

verfasst von : Hui Zou, Ji-Xiang Du, Chuan-Min Zhai, Jing Wang

Erschienen in: Intelligent Computing Theories and Application

Verlag: Springer International Publishing

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Abstract

An increasing number of different multimedia information, including text, voice, video and image, are used to describe the same semantic concept together on the Internet. This paper presents a new method to more efficiently cross-modal multimedia retrieval. Using image and text as an example, we learn the deep learning features of images by convolution neural networks, and learn the text features by a latent Dirichlet allocation model. Then map the two features spaces into a shared presentation space by a probability model in order that they are isomorphic. At last, we adopt centered correlation to measure the distance between them. The experimental results in the Wikipedia dataset show that our approach can achieve the state-of-the-art results.

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Vorheriges Kapitel Natural Scene Digit Classification Using Convolutional Neural Networks
Nächstes Kapitel Leaf Classification Utilizing a Convolutional Neural Network with a Structure of Single Connected Layer
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Metadaten
Titel
Deep Learning and Shared Representation Space Learning Based Cross-Modal Multimedia Retrieval
verfasst von
Hui Zou
Ji-Xiang Du
Chuan-Min Zhai
Jing Wang
Copyright-Jahr
2016
Buch
Intelligent Computing Theories and Application

Print ISBN: 978-3-319-42293-0

Electronic ISBN: 978-3-319-42294-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-42294-7_28

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