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Exploiting tf-idf in deep Convolutional Neural Networks for Content Based Image Retrieval

Exploiting tf-idf in deep Convolutional Neural Networks for Content Based Image Retrieval In this paper, a novel term frequency-inverse document frequency (tf-idf) based method that utilizes deep Convolutional Neural Networks (CNN) for Content Based Image Retrieval (CBIR) is proposed. That is, we treat the learned filters of the convolutional layers of a CNN model as detectors of visual words. Each of these filters has been trained to be activated in different visual patterns. Thus, since the activations of each filter provide information about the degree of presence of the visual pattern that the filter has learned during the training procedure, we consider the activations of these filters as the tf part. Subsequently, we propose three approaches of computing the idf part. Finally, we propose a query expansion technique on top of the formulated descriptors. The proposed approach interconnects the standard tf-idf method with the modern CNN analysis for visual content, providing a very powerful image retrieval technique with improved results as it is highlighted by extensive experiments in four challenging image datasets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Exploiting tf-idf in deep Convolutional Neural Networks for Content Based Image Retrieval

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References (50)

Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
DOI
10.1007/s11042-018-6212-1
Publisher site
See Article on Publisher Site

Abstract

In this paper, a novel term frequency-inverse document frequency (tf-idf) based method that utilizes deep Convolutional Neural Networks (CNN) for Content Based Image Retrieval (CBIR) is proposed. That is, we treat the learned filters of the convolutional layers of a CNN model as detectors of visual words. Each of these filters has been trained to be activated in different visual patterns. Thus, since the activations of each filter provide information about the degree of presence of the visual pattern that the filter has learned during the training procedure, we consider the activations of these filters as the tf part. Subsequently, we propose three approaches of computing the idf part. Finally, we propose a query expansion technique on top of the formulated descriptors. The proposed approach interconnects the standard tf-idf method with the modern CNN analysis for visual content, providing a very powerful image retrieval technique with improved results as it is highlighted by extensive experiments in four challenging image datasets.

Journal

Multimedia Tools and Applications – Springer Journals

Published: Jun 1, 2018

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