@inproceedings{6c946539f95640f6bd806a91a65053ce,
title = "COFENET: CO-FEATURE NEURAL NETWORK MODEL FOR FINE-GRAINED IMAGE CLASSIFICATION",
abstract = "It is challenging to classify patterns with small inter-class variations but large intra-class variations especially for textured objects with relatively small sizes and blurry boundaries. We propose the Co-Feature Network (COFENet), a novel deep learning network for fine-grained texture-based image classification. State-of-the-art (SoTA) methods on this mostly rely on feature concatenation by merging convolutional features into fully connected layers. Some existing work explored the variation between pair-wise features during learning, they only considered the relations in the feature channels, and did not explore the spatial or structural relations among the image regions where the features are extracted from. We propose to leverage such information among the features and their relative spatial layouts to capture richer pairwise, orientationwise, and distancewise relations among feature channels for end-to-end learning of intra-class and inter-class variations.",
keywords = "Fine-grained classification, Image classification, medical image analysis, Texture classification",
author = "Wang, {Bor Shiun} and Hsieh, {Jun Wei} and Hsieh, {Yi Kuan} and Chen, {Ping Yang}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 29th IEEE International Conference on Image Processing, ICIP 2022 ; Conference date: 16-10-2022 Through 19-10-2022",
year = "2022",
doi = "10.1109/ICIP46576.2022.9897463",
language = "English",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "3928--3932",
booktitle = "2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings",
address = "United States",
}