Paper Info
Title
The effectiveness of image features based on fractal image coding for image annotation
Abstract
Image annotation is a process of assigning metadata to digital images in the form of captions or keywords, and has been regarded as image management and one of the most crucial processes of image retrieval. And many automatic methods have been proposed. However, these methods still have some problems respectively. Fractals are fragmented geometries and can be considered separate parts; each part is similar to the contracted overall shape. Fractal features provide geometric information of an image that is irrelevant to the shape and size of an object in the image; therefore, fractal features are more robust than color and texture features. Therefore, this study proposed a fractal-driven image annotation (FIA) schema that extracts fractal features through fractal image coding and integrates color and texture as new visual features to conduct image-based annotation. Experimental results indicate that the effect of thresholds on annotating accuracy is insignificant. This finding supports the application of FIA on complex practical environments, reduces the time for identifying the optimal thresholds, and improves the practicality of using FIA in real environments.
Year
DOI
Venue
2012
10.1016/j.eswa.2012.05.003
Expert Syst. Appl.
Keywords
Field
DocType
fractal image coding,fractal-driven image annotation,fractal feature,digital image,image-based annotation,overall shape,image retrieval,image annotation,image management,extracts fractal feature
Computer vision,Automatic image annotation,Feature detection (computer vision),Fractal compression,Pattern recognition,Computer science,Image texture,Binary image,Image retrieval,Image processing,Artificial intelligence,Digital image processing
Journal
Volume
Issue
ISSN
39
17
0957-4174
Citations 
PageRank 
References 
1
0.35
24
Authors
4
Name
Order
Citations
PageRank
Cho-Wei Shih1131.59
Hui-Chuan Chu220615.90
Yuh-Min Chen337932.12
Chuin-Cheng Wen410.35