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Recently, image representation built upon Convolutional Neural Network (CNN)has been shown to provide effective descriptors for image search, outperformingpre-CNN features as short-vector representations. Yet such models are notcompatible with geometry-aware re-ranking methods and still outperformed, onsome particular object retrieval benchmarks, by traditional image searchsystems relying on precise descriptor matching, geometric re-ranking, or queryexpansion. This work revisits both retrieval stages, namely initial search andre-ranking, by employing the same primitive information derived from the CNN.We build compact feature vectors that encode several image regions without theneed to feed multiple inputs to the network. Furthermore, we extend integralimages to handle max-pooling on convolutional layer activations, allowing us toefficiently localize matching objects. The resulting bounding box is finallyused for image re-ranking. As a result, this paper significantly improvesexisting CNN-based recognition pipeline: We report for the first time resultscompeting with traditional methods on the challenging Oxford5k and Paris6kdatasets.

Particular object retrieval with integral max-pooling of CNN activations