Paper Info
Title
Web image retrieval using majority-based ranking approach
Abstract
Web image retrieval has characteristics different from typical content-based image retrieval; web images have associated textual cues. However, a web image retrieval system often yields undesirable results, because it uses limited text information such as surrounding text, URLs, and image filenames. In this paper, we propose a new approach to retrieval, which uses the image content of retrieved results without relying on assistance from the user. Our basic hypothesis is that more popular images have a higher probability of being the ones that the user wishes to retrieve. According to this hypothesis, we propose a retrieval approach that is based on a majority of the images under consideration. We define four methods for finding the visual features of majority of images; (1) majority-first method, (2) centroid-of-all method, (3) centroid-of-top K method, and (4) centroid-of-largest-cluster method. In addition, we implement a graph/picture classifier for improving the effectiveness of web image retrieval. We evaluate the retrieval effectiveness of both our methods and conventional ones by using precision and recall graphs. Experimental results show that the proposed methods are more effective than conventional keyword-based retrieval methods.
Year
DOI
Venue
2006
10.1007/s11042-006-0039-x
Multimedia Tools Appl.
Keywords
Field
DocType
Web image retrieval,Content-based image retrieval,Image clustering,Image ranking,Graph/picture classifier
Automatic image annotation,Pattern recognition,Information retrieval,Image texture,Computer science,Precision and recall,Image retrieval,Artificial intelligence,Term Discrimination,Adversarial information retrieval,Content-based image retrieval,Visual Word
Journal
Volume
Issue
ISSN
31
2
1380-7501
Citations 
PageRank 
References 
6
0.46
22
Authors
3
Name
Order
Citations
PageRank
Gunhan Park1523.58
Yunju Baek215623.54
Heung-kyu Lee3101687.53