COFENET: CO-FEATURE NEURAL NETWORK MODEL FOR FINE-GRAINED IMAGE CLASSIFICATION

Bor Shiun Wang, Jun Wei Hsieh, Yi Kuan Hsieh, Ping Yang Chen

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
發行者IEEE Computer Society
頁面3928-3932
頁數5
ISBN(電子)9781665496209
DOIs
出版狀態Published - 2022
事件29th IEEE International Conference on Image Processing, ICIP 2022 - Bordeaux, 法國
持續時間: 16 10月 202219 10月 2022

出版系列

名字Proceedings - International Conference on Image Processing, ICIP
ISSN(列印)1522-4880

Conference

Conference29th IEEE International Conference on Image Processing, ICIP 2022
國家/地區法國
城市Bordeaux
期間16/10/2219/10/22

指紋

深入研究「COFENET: CO-FEATURE NEURAL NETWORK MODEL FOR FINE-GRAINED IMAGE CLASSIFICATION」主題。共同形成了獨特的指紋。

引用此