Image retrieval can be formulated as a ranking problem where the goal is to order database images by decreasing similarity to the query. Recent deep models for image retrieval have outperformed traditional methods by leveraging ranking-tailored loss functions, but important theoretical and practical problems remain. First, rather than directly optimizing the global ranking, they minimize an upper-bound on the essential loss, which does not necessarily result in an optimal mean average precision (mAP). Second, these methods require significant engineering efforts to work well, e.g. special pre-training and hard-negative mining. In this paper we propose instead to directly optimize the global mAP by leveraging recent advances in listwise loss formulations. Using a histogram binning approximation, the AP can be differentiated and thus employed to end-to-end learning. Compared to existing losses, the proposed method considers thousands of images simultaneously at each iteration and eliminates the need for ad hoc tricks. It also establishes a new state of the art on many standard retrieval benchmarks. Models and evaluation scripts have been made available at https://europe.naverlabs.com/Deep-Image-Retrieval/
Optimizing Top Precision Performance Measure of Content-Based Image Retrieval by Learning Similarity Function
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Centerfield Corporation（Centerfield公司）；Cornell University（康奈尔大学）；Shandong Medical College（山东医学高等专科学校）；New York University Abu Dhabi（纽约大学阿布扎比分校）；Soochow University（苏州大学）；California State University San Bernardino（加州州立大学圣贝纳迪诺分校）；Travelers Canada（旅行者加拿大公司）
Image retrieval approach based on local texture information derived from predefined patterns and spatial domain information
Faculty of computer engineering, Najafabad Branch, Islamic azad university（伊斯兰阿扎德大学纳贾法巴德分校计算机工程学院）；Big data research center, Najafabad Branch, Islamic azad university（伊斯兰阿扎德大学纳贾法巴德分校大数据研究中心）
