DeepIndex for Accurate and Efficient Image Retrieval
Recently, the success of deep features extracted from convolutional neural networks(CNN) has shown promising results toward bridging the semantic gap. Inspired by this, we attempt to introduce deep features into inverted index based image retrieval and thus propose the DeepIndex framework. Moreover, considering the compensation of different deep features, we incorporate multiple deep features from different fully connected layers, resulting in the multiple DeepIndex. We find the optimal integration of one mid-level deep feature and one high-level deep feature, from two different CNN architectures separately. This can be treated as an attempt to further reduce the semantic gap. Extensive experiments on three benchmark datasets demonstrate that, the proposed DeepIndex method is competitive with the state-of-the-art on Holidays(85.65% mAP), Paris(81.24% mAP), and UKB(3.76 score). In addition, our method is efficient in terms of both memory and time cost.
- The overview of 1-D DeepIndex:
- The pipeline of 2-D DeepIndex with global image signature:
2-D DeepIndex returns more positive image candidates than the 1-D DeepIndex.
Please cite the paper as:
author = "Yu Liu and Yanming Guo and Song Wu and Michael S. Lew",
title = "DeepIndex for Accurate and Efficient Image Retrieval",
booktitle = "ACM International Conference on Multimedia Retrieval (ICMR)",
year = "2015",
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