Similarity Comparisons for Interactive Fine-Grained Categorization
Current human-in-the-loop fine-grained visual categorization systems depend on a predefined vocabulary of attributes and parts, usually determined by experts. In this work, we move away from that expert-driven and attributecentric paradigm and present a novel interactive classifi- cation system that incorporates computer vision and perceptual similarity metrics in a unified framework. At test time, users are asked to judge relative similarity between a query image and various sets of images; these general queries do not require expert-defined terminology and are applicable to other domains and basic-level categories, enabling a flexible, efficient, and scalable system for finegrained categorization with humans in the loop. Our system outperforms existing state-of-the-art systems for relevance feedback-based image retrieval as well as interactive classi- fication, resulting in a reduction of up to 43% in the average number of questions needed to correctly classify an image.
Computer Vision and Pattern Recognition (CVPR), Columbus, OH, 2014.