Paper Info
Title
Scenery Image Retrieval by Meta-Feature Representation.
Abstract
Purpose - Content-based image retrieval suffers from the semantic gap problem: that images are represented by low-level visual features, which are difficult to directly match to high-level concepts in the user's mind during retrieval. To date, visual feature representation is still limited in its ability to represent semantic image content accurately. This paper seeks to address these issues. Design/methodology/approach - In this paper the authors propose a novel meta-feature feature representation method for scenery image retrieval. In particular some class-specific distances (namely meta-features) between low-level image features are measured. For example the distance between an image and its class centre, and the distances between the image and its nearest and farthest images in the same class, etc. Findings - Three experiments based on 190 concrete, 130 abstract, and 61.0 categories in the Carel dataset show that the meta-features extracted from both global and local visual features significantly outperform the original visual features in terms of mean average precision. Originality/value - Compared with traditional local and global low-level features, the proposed meta-features have higher discriminative power for distinguishing a large number of conceptual categories for scenery image retrieval. In addition the meta-features can he directly applied to other image descriptors, such as bag-of-words and contextual features
Year
DOI
Venue
2012
10.1108/14684521211254040
ONLINE INFORMATION REVIEW
Keywords
Field
DocType
Image retrieval,Feature extraction,Feature representation,Class-specific distances,Meta-features,Digital images,Image processing
Feature detection (computer vision),Computer science,Image processing,Image retrieval,Artificial intelligence,Computer vision,Automatic image annotation,Information retrieval,Pattern recognition,Feature (computer vision),Image texture,Feature extraction,Visual Word
Journal
Volume
Issue
ISSN
36.0
4.0
1468-4527
Citations 
PageRank 
References 
0
0.34
18
Authors
2
Name
Order
Citations
PageRank
Chih-fong Tsai1125554.93
Wei-Chao Lin2655.90