No Spare Parts: Sharing Part Detectors for Image Categorization

This work aims for image categorization using a representation of distinctive parts. Different from existing part-based work, we argue that parts are naturally shared between image categories and should be modeled as such. We motivate our approach with a quantitative and qualitative analysis by backtracking where selected parts come from. Our analysis shows that in addition to the category parts defining the class, the parts coming from the background context and parts from other image categories improve categorization performance. Part selection should not be done separately for each category, but instead be shared and optimized over all categories. To incorporate part sharing between categories, we present an algorithm based on AdaBoost to jointly optimize part sharing and selection, as well as fusion with the global image representation. We achieve results competitive to the state-of-the-art on object, scene, and action categories, further improving over deep convolutional neural networks

Exemplar-Specific Patch Features for Fine-Grained Recognition

Abstract. In this paper, we present a new approach for fine-grained recognition or subordinate categorization, tasks where an algorithm needs to reliably differ-entiate between visually similar categories, e.g., different bird species. While pre-vious approaches aim at learning a single generic representation and models with increasing complexity, we propose an orthogonal approach that learns patch rep-resentations specifically tailored to every single test exemplar. Since we query a constant number of images similar to a given test image, we obtain very com-pact features and avoid large-scale training with all classes and examples. Our learned mid-level features are built on shape and color detectors estimated from discovered patches reflecting small highly discriminative structures in the queried images. We evaluate our approach for fine-grained recognition on the CUB-2011 birds dataset and show that high recognition rates can be obtained by model com-bination.