Tampere University of Technology

TUTCRIS Research Portal

Simultenious binary hash and features learning for image retrieval

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationMobile Multimedia/Image Processing, Security, and Applications 2016
PublisherSPIE
ISBN (Electronic)9781510601109
DOIs
Publication statusPublished - 2016
Publication typeA4 Article in a conference publication
EventMobile Multimedia/Image Processing, Security, and Applications -
Duration: 1 Jan 2000 → …

Publication series

NameSPIE Conference Proceedings
Volume9869
ISSN (Print)0277-786X

Conference

ConferenceMobile Multimedia/Image Processing, Security, and Applications
Period1/01/00 → …

Abstract

Content-based image retrieval systems have plenty of applications in modern world. The most important one is the image search by query image or by semantic description. Approaches to this problem are employed in personal photo-collection management systems, web-scale image search engines, medical systems, etc. Automatic analysis of large unlabeled image datasets is virtually impossible without satisfactory image-retrieval technique. It's the main reason why this kind of automatic image processing has attracted so much attention during recent years. Despite rather huge progress in the field, semantically meaningful image retrieval still remains a challenging task. The main issue here is the demand to provide reliable results in short amount of time. This paper addresses the problem by novel technique for simultaneous learning of global image features and binary hash codes. Our approach provide mapping of pixel-based image representation to hash-value space simultaneously trying to save as much of semantic image content as possible. We use deep learning methodology to generate image description with properties of similarity preservation and statistical independence. The main advantage of our approach in contrast to existing is ability to fine-tune retrieval procedure for very specific application which allow us to provide better results in comparison to general techniques. Presented in the paper framework for data- dependent image hashing is based on use two different kinds of neural networks: convolutional neural networks for image description and autoencoder for feature to hash space mapping. Experimental results confirmed that our approach has shown promising results in compare to other state-of-the-art methods.

Keywords

  • autoencoder, content{based image retrieval, deep convolutional neural network, semantic hashing

Publication forum classification

Field of science, Statistics Finland