Capturing Long-Tail Distributions of Object Subcategories

• Published in: 2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 915 - 922
• Main Authors: Xiangxin Zhu, Anguelov, Dragomir, Ramanan, Deva
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.06.2014
• Subjects: Accuracy; Algorithms; Clustering algorithms; Computational modeling; Computer vision; Conferences; Force; Learning; Mathematical models; Optimization; Pattern recognition; Searching; Training; Trains; Visualization; Volatile organic compounds

We argue that object subcategories follow a long-tail distribution: a few subcategories are common, while many are rare. We describe distributed algorithms for learning large- mixture models that capture long-tail distributions, which are hard to model with current approaches. We introduce a general...