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Visual instance retrieval with deep convolutional networks
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
KTH.
KTH.
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.ORCID iD: 0000-0002-4266-6746
2016 (English)In: ITE Transactions on Media Technology and Applications, ISSN 2186-7364, Vol. 4, no 3, 251-258 p.Article in journal (Refereed) Published
Abstract [en]

This paper provides an extensive study on the availability of image representations based on convolutional networks (ConvNets) for the task of visual instance retrieval. Besides the choice of convolutional layers, we present an efficient pipeline exploiting multi-scale schemes to extract local features, in particular, by taking geometric invariance into explicit account, i.e. positions, scales and spatial consistency. In our experiments using five standard image retrieval datasets, we demonstrate that generic ConvNet image representations can outperform other state-of-the-art methods if they are extracted appropriately.

Place, publisher, year, edition, pages
Institute of Image Information and Television Engineers , 2016. Vol. 4, no 3, 251-258 p.
Keyword [en]
Convolutional network, Learning representation, Multi-resolution search, Visual instance retrieval
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-195472Scopus ID: 2-s2.0-84979503481OAI: oai:DiVA.org:kth-195472DiVA: diva2:1049836
Note

QC 20161125

Available from: 2016-11-25 Created: 2016-11-03 Last updated: 2016-12-09Bibliographically approved
In thesis
1. Convolutional Network Representation for Visual Recognition
Open this publication in new window or tab >>Convolutional Network Representation for Visual Recognition
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Image representation is a key component in visual recognition systems. In visual recognition problem, the solution or the model should be able to learn and infer the quality of certain visual semantics in the image. Therefore, it is important for the model to represent the input image in a way that the semantics of interest can be inferred easily and reliably. This thesis is written in the form of a compilation of publications and tries to look into the Convolutional Networks (CovnNets) representation in visual recognition problems from an empirical perspective. Convolutional Network is a special class of Neural Networks with a hierarchical structure where every layer’s output (except for the last layer) will be the input of another one. It was shown that ConvNets are powerful tools to learn a generic representation of an image. In this body of work, we first showed that this is indeed the case and ConvNet representation with a simple classifier can outperform highly-tuned pipelines based on hand-crafted features. To be precise, we first trained a ConvNet on a large dataset, then for every image in another task with a small dataset, we feedforward the image to the ConvNet and take the ConvNets activation on a certain layer as the image representation. Transferring the knowledge from the large dataset (source task) to the small dataset (target task) proved to be effective and outperformed baselines on a variety of tasks in visual recognition. We also evaluated the presence of spatial visual semantics in ConvNet representation and observed that ConvNet retains significant spatial information despite the fact that it has never been explicitly trained to preserve low-level semantics. We then tried to investigate the factors that affect the transferability of these representations. We studied various factors on a diverse set of visual recognition tasks and found a consistent correlation between the effect of those factors and the similarity of the target task to the source task. This intuition alongside the experimental results provides a guideline to improve the performance of visual recognition tasks using ConvNet features. Finally, we addressed the task of visual instance retrieval specifically as an example of how these simple intuitions can increase the performance of the target task massively.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2017. 130 p.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 2017:01
Keyword
Convolutional Network, Visual Recognition, Transfer Learning
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-197919 (URN)978-91-7729-213-5 (ISBN)
Public defence
2017-01-13, F3, Lindstedtsvagen 26, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20161209

Available from: 2016-12-09 Created: 2016-12-09 Last updated: 2016-12-23Bibliographically approved

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Output format
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