Paper Info
Title
Un-Supervised, Semi-Supervised and Supervised Image Retrieval Based on Conceptual Features
Abstract
To effectively and efficiently retrieve desired images from a large image database, an intuitional and common type of approaches is text-based image retrieval which accesses the images by comparing conceptual terms of a query and image data. Unfortunately, this type of image retrieval is not easy to earn users' satisfactions due to the problem of the image database maintenance. Another useful type is content-based image retrieval which determines the most relevant images according to the content comparisons between a query image and searched images. Although the content-based image retrieval can avoid the problems of text-based image retrieval, it cannot certainly generate good results because of the semantic gap between low-level visual features and high-level concepts. Therefore, in this paper, we propose three types of content-based image retrieval and a conceptual feature to improve the quality of content-based image retrieval. The experimental results show that, the supervised image retrieval using the proposed conceptual features can bring out better retrieval results than the traditional image retrieval using low-level visual features.
Year
DOI
Venue
2016
10.1109/BigMM.2016.26
2016 IEEE Second International Conference on Multimedia Big Data (BigMM)
Keywords
Field
DocType
content-based,image retrieval,conceptual features,un-supervised,semi-supervised,supervised
Human–computer information retrieval,Automatic image annotation,Pattern recognition,Feature detection (computer vision),Information retrieval,Computer science,Image texture,Semantic gap,Image retrieval,Feature extraction,Artificial intelligence,Visual Word
Conference
ISBN
Citations 
PageRank 
978-1-5090-2180-2
0
0.34
References 
Authors
15
4
Name
Order
Citations
PageRank
Ja-Hwung Su132924.53
Tzung-pei Hong23768483.06
Yu-Tang Chang300.34
Hsu-Yuan Tung400.34