Unsupervised Parallel Extraction based Texture for Efficient Image Representation

Autor:	Abdelsamea, Mohammed M.
Rok vydání:	2014
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Zdroj:	2011 International Conference on Signal, Image Processing and Applications With workshop of ICEEA 2011, IPCSIT vol.21 (2011), IACSIT Press, Singapore
Druh dokumentu:	Working Paper
Popis:	SOM is a type of unsupervised learning where the goal is to discover some underlying structure of the data. In this paper, a new extraction method based on the main idea of Concurrent Self-Organizing Maps (CSOM), representing a winner-takes-all collection of small SOM networks is proposed. Each SOM of the system is trained individually to provide best results for one class only. The experiments confirm that the proposed features based CSOM is capable to represent image content better than extracted features based on a single big SOM and these proposed features improve the final decision of the CAD. Experiments held on Mammographic Image Analysis Society (MIAS) dataset. Comment: arXiv admin note: substantial text overlap with arXiv:1408.4143
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1408.4504 Zobrazit plný text záznamu View this record from Arxiv