Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/104453
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Type: Journal article
Title: Mining Mid-level Visual Patterns with Deep CNN Activations
Author: Li, Y.
Liu, L.
Shen, C.
Hengel, A.
Citation: International Journal of Computer Vision, 2017; 121(3):344-364
Publisher: Springer
Issue Date: 2017
ISSN: 0920-5691
1573-1405
Statement of
Responsibility: 
Yao Li, Lingqiao Liu, Chunhua Shen, Anton van den Hengel
Abstract: The purpose of mid-level visual element discovery is to find clusters of image patches that are representative of, and which discriminate between, the contents of the relevant images. Here we propose a pattern-mining approach to the problem of identifying mid-level elements within images, motivated by the observation that such techniques have been very effective, and efficient, in achieving similar goals when applied to other data types. We show that Convolutional Neural Network (CNN) activations extracted from image patches typical possess two appealing properties that enable seamless integration with pattern mining techniques. The marriage between CNN activations and a pattern mining technique leads to fast and effective discovery of representative and discriminative patterns from a huge number of image patches, from which mid-level elements are retrieved. Given the patterns and retrieved mid-level visual elements, we propose two methods to generate image feature representations. The first encoding method uses the patterns as codewords in a dictionary in a manner similar to the Bag-of-Visual-Words model. We thus label this a Bag-of-Patterns representation. The second relies on mid-level visual elements to construct a Bag-of-Elements representation. We evaluate the two encoding methods on object and scene classification tasks, and demonstrate that our approach outperforms or matches the performance of the state-of-the-arts on these tasks.
Keywords: Mid-level visual element discovery; pattern mining; convolutional neural networks
Rights: © Springer Science+Business Media New York 2016
RMID: 0030054973
DOI: 10.1007/s11263-016-0945-y
Grant ID: http://purl.org/au-research/grants/arc/FT120100969
Appears in Collections:Computer Science publications

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