Machine Learning
Accelerating fractal image compression by multi-dimensional nearest neighbor search
DCC '95 Proceedings of the Conference on Data Compression
Using One-Class and Two-Class SVMs for Multiclass Image Annotation
IEEE Transactions on Knowledge and Data Engineering
A graph-based image annotation framework
Pattern Recognition Letters
A survey of methods for image annotation
Journal of Visual Languages and Computing
Automatic Semantic Annotation of Real-World Web Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection based-on genetic algorithm for image annotation
Knowledge-Based Systems
TSVM-HMM: Transductive SVM based hidden Markov model for automatic image annotation
Expert Systems with Applications: An International Journal
Automatic web image annotation for image retrieval systems
ICS'08 Proceedings of the 12th WSEAS international conference on Systems
Novel fractal image encoding algorithm using normalized one-norm and kick-out condition
Image and Vision Computing
An improved no-search fractal image coding method based on a fitting plane
Image and Vision Computing
Baselines for Image Annotation
International Journal of Computer Vision
Real time fractal image coder based on characteristic vector matching
Image and Vision Computing
Fusing semantic aspects for image annotation and retrieval
Journal of Visual Communication and Image Representation
Modeling continuous visual features for semantic image annotation and retrieval
Pattern Recognition Letters
An energy-based model for region-labeling
Computer Vision and Image Understanding
Automatic image annotation by mining the web
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Bridging the Gap: Query by Semantic Example
IEEE Transactions on Multimedia
Speed-up in fractal image coding: comparison of methods
IEEE Transactions on Image Processing
Image classification for content-based indexing
IEEE Transactions on Image Processing
Hi-index | 12.05 |
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.