Abstract
This Paper Presents a Comprehensive Methodology for General Large-Scale Image-Based Classification Tasks. It Addresses the Big Data Challenge in Arbitrary Image Classification and More Specifically, Filtering of Millions of Websites with Abstract Target Classes and High Levels of Label Noise. Our Approach Uses Local Image Features and their Color Descriptors to Build Image Representations with the Help of a Modified K-Nn Algorithm. Image Representations Are Refined into Image and Website Class Predictions by a Two-Stage Classifier Method Suitable for a Very Large-Scale Real Dataset. a Modification of an Extreme Learning Machine is Found to Be a Suitable Classifier Technique. the Methodology is Robust to Noise and Can Learn Abstract Target Categories; Website Classification Accuracy Surpasses 97% for the Most Important Categories Considered in This Study.
Recommended Citation
A. Akusok et al., "Arbitrary Category Classification of Websites based on Image Content," IEEE Computational Intelligence Magazine, vol. 10, no. 2, pp. 30 - 41, article no. 7083681, Institute of Electrical and Electronics Engineers, May 2015.
The definitive version is available at https://doi.org/10.1109/MCI.2015.2405317
Department(s)
Engineering Management and Systems Engineering
International Standard Serial Number (ISSN)
1556-603X
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 May 2015
