Image Retrieval using Multi-scale CNN Features Pooling
Image Retrieval using Multi-scale CNN Features Pooling
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.
Accepted at ICMR 2020
- New York University Italy
- University of Macerata Italy
- University of Florence Italy
- University of Pisa Italy
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, CBIR, CNN, Image retrieval, Max-pooling, NetVLAD, CBIR; CNN; Image retrieval; Max-pooling; NetVLAD
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, CBIR, CNN, Image retrieval, Max-pooling, NetVLAD, CBIR; CNN; Image retrieval; Max-pooling; NetVLAD
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