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Improved VLAD with Multiple Codebooks and Deep Convolutional Descriptors

The aggregate descriptors based on handcraft patch-level features have been successfully applied to content-based image retrieval. However, that most of these handcraft patch-level features lost semantic information limit the performance of aggregate descriptors. In this paper, we present an approach to encode the aggregate descriptors by off-the-shelf deep convolutional neural networks (CNNs) which is trained with lots of semantic information. We first utilize the CNNs model to compute the outputs of multiple convolutional layers as patch-level features, then encode these features that come from different intermediate layers into multiple codebooks to construct the aggregate image descriptors. Experiments demonstrate that our multiple aggregate image descriptors obtains state-of-the-art results.