Image Retrieval using Multi-scale CNN Features Pooling
This work addresses image retrieval for computer vision applications, presenting an incremental improvement over existing methods.
The paper tackles image retrieval by learning image representations using a CNN with multi-scale local pooling and triplet mining, achieving state-of-the-art results on three standard datasets.
In this paper, we address the problem of image retrieval by learning images representation based on the activations of a Convolutional Neural Network. We present an end-to-end trainable network architecture that exploits a novel multi-scale local pooling based on NetVLAD and a triplet mining procedure based on samples difficulty to obtain an effective image representation. Extensive experiments show that our approach is able to reach state-of-the-art results on three standard datasets.