Co-occurrence of deep convolutional features for image search
Abstract
Image search can be tackled using deep features from pre-trained Convolutional Neural Networks (CNN). The feature map from the last convolutional layer of a CNN encodes descriptive information from which a discriminative global descriptor can be obtained. We propose a new representation of co-occurrences from deep convolutional features to extract additional relevant information from this last convolutional layer. Combining this co-occurrence map with the feature map, we achieve an improved image representation. We present two different methods to get the co-occurrence representation, the first one based on direct aggregation of activations, and the second one, based on a trainable co-occurrence representation. The image descriptors derived from our methodology improve the performance in very well-known image retrieval datasets as we prove in the experiments.
Keywords
Cite
@article{arxiv.2003.13827,
title = {Co-occurrence of deep convolutional features for image search},
author = {J. I. Forcen and Miguel Pagola and Edurne Barrenechea and Humberto Bustince},
journal= {arXiv preprint arXiv:2003.13827},
year = {2021}
}