RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps

• URL: http://arxiv.org/abs/2303.04554v1
• Date: Wed, 8 Mar 2023 13:09:03 GMT
• Title: RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps
• Authors: Leonardo Scabini, Kallil M. Zielinski, Lucas C. Ribas, Wesley N. Gon\c{c}alves, Bernard De Baets, Odemir M. Bruno
• Abstract summary: We propose a new method named textbfRandom encoding of textbfAggregated textbfDeep textbfActivation textbfMaps (RADAM) RADAM extracts rich texture representations without ever changing the backbone. Our results suggest that pre-trained backbones may not require additional fine-tuning for texture recognition if their learned representations are better encoded.
• Score: 12.35895883721336
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Texture analysis is a classical yet challenging task in computer vision for which deep neural networks are actively being applied. Most approaches are based on building feature aggregation modules around a pre-trained backbone and then fine-tuning the new architecture on specific texture recognition tasks. Here we propose a new method named \textbf{R}andom encoding of \textbf{A}ggregated \textbf{D}eep \textbf{A}ctivation \textbf{M}aps (RADAM) which extracts rich texture representations without ever changing the backbone. The technique consists of encoding the output at different depths of a pre-trained deep convolutional network using a Randomized Autoencoder (RAE). The RAE is trained locally to each image using a closed-form solution, and its decoder weights are used to compose a 1-dimensional texture representation that is fed into a linear SVM. This means that no fine-tuning or backpropagation is needed. We explore RADAM on several texture benchmarks and achieve state-of-the-art results with different computational budgets. Our results suggest that pre-trained backbones may not require additional fine-tuning for texture recognition if their learned representations are better encoded.

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