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Selective Deep Convolutional Features for Image Retrieval by Hoang, Tuan; Do, Thanh-Toan; Tan, Dang-Khoa Le; Cheung, Ngai-Man is a scholarly article available to read on EtoBox.

What is Selective Deep Convolutional Features for Image Retrieval about?

Convolutional Neural Network (CNN) is a very powerful approach to extract discriminative local descriptors for effective image search. Recent work adopts fine-tuned strategies to further improve the discriminative power of the descriptors. Taking a different approach, in this paper, we propose a novel framework to achieve competitive retrieval performance. Firstly, we propose various masking schemes, namely SIFT-mask, SUM-mask, and MAX-mask, to select a representative subset of local convolutional features and remove a large number of redundant features. We demonstrate that this can effectively address the burstiness issue and improve retrieval accuracy. Secondly, we propose to employ recent embedding and aggregating methods to further enhance feature discriminability. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art retrieval accuracy.

Author

Hoang, Tuan; Do, Thanh-Toan; Tan, Dang-Khoa Le; Cheung, Ngai-Man

Published

2017

Language

EN