Scenery image retrieval by meta-feature representation

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 i...

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Bibliographic Details
Published in:Online information review Vol. 36; no. 4; pp. 517 - 533
Main Authors: Tsai, Chih-Fong, Lin, Wei-Chao
Format: Journal Article
Language:English
Published: Bradford Emerald Group Publishing Limited 01.01.2012
Emerald
Subjects:
Algorithms
Averages
Categories
Classification
Coding
Computer Graphics
Decomposition
Exact sciences and technology
Experiments
Global local relationship
Image retrieval
Indexing
Information and communication sciences
Information content
Information science. Documentation
Library and information science. General aspects
Methods
On-line systems
Pattern Recognition
Representation
Representations
Research Problems
Retrieval
Scenery
Sciences and techniques of general use
Search engines
Semantics
Semiotics
Studies
Visual
Wavelet transforms
Word meaning
Class-specific distances
Digital images
Image processing
Image retrieval
Feature extraction
Feature representation
Meta-features
Extraction
Representation
Digital image
Distance
ISSN:1468-4527, 1468-4535
Online Access:Get full text
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Summary: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 610 categories in the Corel 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 be directly applied to other image descriptors, such as bag-of-words and contextual features.
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ISSN:1468-4527
1468-4535
DOI:10.1108/14684521211254040