Task-Driven Progressive Part Localization for Fine-Grained Recognition

Abstract

In this paper we propose a task-driven progressive part localization (TPPL) approach for fine-grained object recognition. Most existing methods follow a two-step approach which first detects salient object parts to suppress the interference from background scenes and then classifies objects based on features extracted from these regions. The part detector and object classifier are often independently designed and trained. In this paper, our major finding is that the part detector should be jointly designed and progressively refined with the object classifier so that the detected regions can provide the most distinctive features for final object recognition. Specifically, we start with a part-based SPP-net (Part-SPP) as our baseline part detector. We then develop a task-driven progressive part localization framework, which takes the predicted boxes of Part-SPP as an initial guess, then examines new regions in the neighborhood, searching for more discriminative image regions to maximize the recognition performance. This procedure is performed in an iterative manner to progressively improve the joint part detection and object classification performance. Experimental results on the Caltech-UCSD-200-2011 dataset demonstrate that our method outperforms state-of-the-art fine-grained categorization methods both in part localization and classification, even without requiring a bounding box during testing.