Paper Info
Title
Online learning to rank for content-based image retrieval
Abstract
A major challenge in Content-Based Image Retrieval (CBIR) is to bridge the semantic gap between low-level image contents and high-level semantic concepts. Although researchers have investigated a variety of retrieval techniques using different types of features and distance functions, no single best retrieval solution can fully tackle this challenge. In a real-world CBIR task, it is often highly desired to combine multiple types of different feature representations and diverse distance measures in order to close the semantic gap. In this paper, we investigate a new framework of learning to rank for CBIR, which aims to seek the optimal combination of different retrieval schemes by learning from large-scale training data in CBIR. We first formulate the problem formally as a learning to rank task, which can be solved in general by applying the existing batch learning to rank algorithms from text information retrieval (IR). To further address the scalability towards large-scale online CBIR applications, we present a family of online learning to rank algorithms, which are significantly more efficient and scalable than classical batch algorithms for large-scale online CBIR. Finally, we conduct an extensive set of experiments, in which encouraging results show that our technique is effective, scalable and promising for large-scale CBIR.
Year
Venue
Field
2015
IJCAI
Training set,Online learning,Learning to rank,Information retrieval,Computer science,Semantic gap,Image retrieval,Artificial intelligence,Content-based image retrieval,Machine learning,Scalability,Distance measures
DocType
Citations 
PageRank 
Conference
3
0.37
References 
Authors
34
8
Name
Order
Citations
PageRank
Ji Wan12468.45
Pengcheng Wu265430.60
Steven C. H. Hoi33830174.61
Peilin Zhao4136580.09
Xingyu Gao510614.95
Dayong Wang651026.86
Yongdong Zhang72544166.91
Jintao Li81488111.30