Deep Neural Networks Fused with Textures for Image Classification
This work addresses fine-grained image classification, a challenging problem in computer vision, but it is incremental as it combines existing techniques like LBP and LSTM for feature fusion.
The paper tackles fine-grained image classification by fusing global texture features from local binary patterns with local patch-based deep features encoded via LSTM, achieving better classification accuracy over existing methods on eight diverse datasets.
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). Another path computes image-level textures at multiple scales using the local binary patterns (LBP). The advantages of both streams are integrated to represent an efficient feature vector for image classification. The method is tested on eight datasets representing the human faces, skin lesions, food dishes, marine lives, etc. using four standard backbone CNNs. Our method has attained better classification accuracy over existing methods with notable margins.