ITE Transactions on Media Technology and Applications
Special Section on Image and Video Analysis, Search, and Benchmark
[Paper] Visual Instance Retrieval with Deep Convolutional Networks
Ali S. RazavianJosephine SullivanStefan CarlssonAtsuto Maki
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Volume 4 (2016) Issue 3 Pages 251-258

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Abstract

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.

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© 2016 The Institute of Image Information and Television Engineers
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