Title :
Compact hashing with joint optimization of search accuracy and time
Author :
He, Junfeng ; Radhakrishnan, Regunathan ; Chang, Shih-Fu ; Bauer, Claus
Author_Institution :
Columbia Univ., New York, NY, USA
Abstract :
Similarity search, namely, finding approximate nearest neighborhoods, is the core of many large scale machine learning or vision applications. Recently, many research results demonstrate that hashing with compact codes can achieve promising performance for large scale similarity search. However, most of the previous hashing methods with compact codes only model and optimize the search accuracy. Search time, which is an important factor for hashing in practice, is usually not addressed explicitly. In this paper, we develop a new scalable hashing algorithm with joint optimization of search accuracy and search time simultaneously. Our method generates compact hash codes for data of general formats with any similarity function. We evaluate our method using diverse data sets up to 1 million samples (e.g., web images). Our comprehensive results show the proposed method significantly outperforms several state-of-the-art hashing approaches.
Keywords :
computer vision; file organisation; image retrieval; information retrieval; learning (artificial intelligence); optimisation; approximate nearest neighborhoods; compact codes; compact hash codes; compact hashing; hashing methods; joint optimization; large scale machine learning; large scale similarity search; machine vision applications; scalable hashing algorithm; search accuracy; search time; similarity function; Accuracy; Complexity theory; Equations; Joints; Kernel; Optimization; Training;
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
Conference_Location :
Providence, RI
Print_ISBN :
978-1-4577-0394-2
DOI :
10.1109/CVPR.2011.5995518