Collaborative Learning of Fine-grained Visual Data

Abstract

Problem: Deep learning based vision systems have achieved near human accuracy in recognizing coarse object categories from visual data. But recognizing fine-grained sub-categories remains an open problem. Tasks like fine-grained species recognition poses further challenges: significant background variation compared to subtle difference between objects, high class imbalance due to scarcity of samples for endangered species, cost of domain expert annotations and labeling, etc.

Methodology: The existing approaches, like transfer learning, to solve the problem of learning small specialized datasets are still inadequate in case of fine-grained sub-categories. The hypothesis of this work is that collaborative filters should be incorporated into the present learning frameworks to better address these challenges. The intuition comes from the fact that collaborative representation based classifiers have been earlier used for face recognition problems which present similar challenges.

Outcomes: Keeping the above hypothesis in mind, the thesis achieves the following objectives: 1) It demonstrates the suitability of collaborative classifiers for fine-grained recognition 2) It expands the state-of-the-art by incorporating automated background suppression into collaborative classification formulation 3) It incorporates the collaborative cost function into supervised learning (deep convolutional network) and unsupervised learning (clustering algorithms) 4) Lastly, during the work several benchmark fine-grained image datasets have been introduced on NZ and Indian butterflies and bird species recognition.