Paper Info
Title
Query Bootstrapping: A Visual Mining Based Query Expansion
Abstract
Bag of Visual Words (BoVW) is an effective framework for image retrieval. Query expansion (QE) further boosts retrieval performance by refining a query with relevant visual words found from the geometric consistency check between the query image and highly ranked retrieved images obtained from the first round of retrieval. Since QE checks the pairwise consistency between query and highly ranked images, its performance may deteriorate when there are slight degradations in the query image. We propose Query Bootstrapping as a variant of QE to circumvent this problem by using the consistency of highly ranked images instead of pairwise consistency. In so doing, we regard frequently co-occurring visual words in highly ranked images as relevant visual words. Frequent itemset mining (FIM) is used to find such visual words efficiently. However, the FIM-based approach requires sensitive parameters to be fine-tuned, namely, support (min/max-support) and the number of top ranked images (top-k). Here, we propose an adaptive support algorithm that adaptively determines both the minimum support and maximum support by referring to the first round's retrieval list. Selecting relevant images by using a geometric consistency check further boosts retrieval performance by reducing outlier images from a mining process. An important parameter for the LO-RANSAC algorithm that is used for the geometric consistency check, namely, inlier threshold, is automatically determined by our algorithm. We further introduce tf-fi-idf on top of tf-idf in order to take into account the frequency of inliers (fi) in the retrieved images. We evaluated the performance of QB in terms of mean average precision (mAP) on three benchmark datasets and found that it gave significant performance boosts of 5.37%, 9.65%, and 8.52% over that of state-of-the-art QE on Oxford 5k, Oxford 105k, and Paris 6k, respectively.
Year
DOI
Venue
2016
10.1587/transinf.2015EDP7193
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
Field
DocType
image retrieval, instance search, query expansion, frequent itemset mining, visual word mining, query bootstrapping, adaptive support, adaptive inlier threshold
Query optimization,Web search query,Query language,Pattern recognition,Query expansion,Information retrieval,Bootstrapping,Computer science,Sargable,Web query classification,Image retrieval,Artificial intelligence
Journal
Volume
Issue
ISSN
E99D
2
1745-1361
Citations 
PageRank 
References 
0
0.34
13
Authors
3
Name
Order
Citations
PageRank
Siriwat Kasamwattanarote192.56
Yusuke Uchida2102.82
Shin'ichi Satoh32093277.41