Symbiotic Segmentation and Part Localization for Fine-Grained Categorization

TLDR: The model builds a model of the base-level category that can be fitted to images, producing high-quality foreground segmentation and mid-level part localizations, and improves the categorization accuracy over the state-of-the-art.

Abstract: We propose a new method for the task of fine-grained visual categorization The method builds a model of the base-level category that can be fitted to images, producing high-quality foreground segmentation and mid-level part localizations The model can be learnt from the typical datasets available for fine-grained categorization, where the only annotation provided is a loose bounding box around the instance (eg bird) in each image Both segmentation and part localizations are then used to encode the image content into a highly-discriminative visual signature The model is symbiotic in that part discovery/localization is helped by segmentation and, conversely, the segmentation is helped by the detection (eg part layout) Our model builds on top of the part-based object category detector of Felzenszwalb et al, and also on the powerful Grab Cut segmentation algorithm of Rother et al, and adds a simple spatial saliency coupling between them In our evaluation, the model improves the categorization accuracy over the state-of-the-art It also improves over what can be achieved with an analogous system that runs segmentation and part-localization independently