Co-occurrence of deep convolutional features for image search

• URL: http://arxiv.org/abs/2003.13827v2
• Date: Thu, 10 Jun 2021 16:47:42 GMT
• Title: Co-occurrence of deep convolutional features for image search
• Authors: J.I.Forcen, Miguel Pagola, Edurne Barrenechea and Humberto Bustince
• Abstract summary: Image search can be tackled using deep features from pre-trained Convolutional Neural Networks (CNN) We propose a new representation of co-occurrences from deep convolutional features to extract additional relevant information from this last convolutional layer.
• Score: 9.030769176986057
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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