Subset Feature Learning for Fine-Grained Category Classification

Fine-grained categorisation has been a challenging problem due to small inter-class variation, large intra-class variation and low number of training images. We propose a learning system which first clusters visually similar classes and then learns deep convolutional neural network features specific to each subset. Experiments on the popular fine-grained Caltech-UCSD bird dataset show that the proposed method outperforms recent fine-grained categorisation methods under the most difficult setting: no bounding boxes are presented at test time. It achieves a mean accuracy of 77.5%, compared to the previous best performance of 73.2%. We also show that progressive transfer learning allows us to first learn domain-generic features (for bird classification) which can then be adapted to specific set of bird classes, yielding improvements in accuracy

Improved vision-based weed classification for robotic weeding – a method for increasing speed while retaining accuracy

In this paper, we demonstrate how a deep convolutional neural network (DCNN) can be deployed in resource limited environments, such as robots, to reduce the inference time by more than an order of magnitude while retaining high classification accuracy and robustness to novel conditions. This is achieved by training a lightweight DCNN, or compressed model, via model distillation. We show that training models using this approach outperform training a similar model from scratch, using the same data, for weed classification. Using model distillation we are able to improve the accuracy from 97.1% to 97.9% for similar conditions (as the training data) and from 86.4% to 89.8% for different conditions (as the training data). This is in comparison to a traditional approach using robust local binary pattern features which achieves 87.7% for classifying in similar conditions and 83.9% for classifying in different conditions. Finally, we compare this compressed model to a complex fine-tuned model which achieves higher accuracy of 99.6% for the same condition and 95.8% for different conditions but has 100.0 times more parameters (larger model size) and is 40.6 times slower at computing the inference

Exploring multi-subset learning techniques for fine-grained food image classification

Fine-grained image recognition (FGIR) is a fundamental and challenging problem within the field of computer vision that involves analyzing visual objects from subordinate categories, such as bird species or car models. The applications of FGIR are plentiful in both industry and research, ranging from automatic biodiversity monitoring to intelligent transportation. Recent advances in deep learning have paved the way for significant progress in this field. A recently proposed method is FGFR, a food-centered fine-grained recognition method that leverages a multitask architecture in which different heads or tasks specialize in discriminating between classes of automatically detected subsets of hard-to-distinguish classes. In this work, we provide an in-depth analysis of the behavior of FGFR and propose an improved version, FGFR+, which builds on top of the limitations we identify from our study of the original method. While we prove that FGFR is capable of generalizing to other non-food domains and different types of backbone architectures, we also observe that the method is not taking full advantage of its specialized multi-head structure. We find that, by implementing a series of conceptually simple modifications, the performance of the method can be significantly boosted, capitalizing on the fine-grained knowledge provided by the heads. FGFR+ achieves 94.2% top-1 validation accuracy on the Food-101 dataset, virtually ranking third in its corresponding benchmark. Being compatible with a wide range of deep learning computer vision backbone architectures, FGFR+ could have the potential of boosting the performance of many computer vision classification tasks