Deep Neural Networks Fused with Textures for Image Classification

arXiv:2308.01813v2 Announce Type: replace
Abstract: Fine-grained image classification (FGIC) is a challenging task in computer vision for due to small visual differences among inter-subcategories, but, large intra-class variations. Deep learning methods have achieved remarkable success in solving FGIC. In this paper, we propose a fusion approach to address FGIC by combining global texture with local patch-based information. The first pipeline extracts deep features from various fixed-size non-overlapping patches and encodes features by sequential modelling using the long short-term memory (LSTM). …

abstract arxiv class classification computer computer vision cs.ai cs.cv deep learning differences fine-grained fusion image networks neural networks paper small success type vision visual