Fast feature selection algorithm for poultry skin tumor detection in hyperspectral data

Autor:	David P. Casasent, Songyot Nakariyakul
Rok vydání:	2009
Předmět:	Branch and bound Computer science Feature extraction Multispectral image Skin tumor ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Hyperspectral imaging Feature selection Algorithm Selection (genetic algorithm) Food Science Constant false alarm rate
Zdroj:	Journal of Food Engineering. 94:358-365
ISSN:	0260-8774
DOI:	10.1016/j.jfoodeng.2009.04.001
Popis:	Hyperspectral reflectance imaging data are analyzed for poultry skin tumor detection. We consider selecting only a few wavebands from hyperspectral data for potential use in a real-time multispectral camera. To do this, we improve our prior tumor detection system by employing our new adaptive branch and bound algorithm and a support vector machine classifier. Our HS analysis is useful since it provides a guideline for selection of the specific wavelengths for best tumor detection (feature selection). Experimental results demonstrate that our optimal adaptive branch and bound algorithm is significantly faster than other versions of the branch and bound algorithm. We compare the performance of our feature selection algorithm to that of a feature extraction algorithm and show that using our feature selection algorithm gives a better tumor detection rate and a lower false alarm rate.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f89d8f7abbb087b9a7c583c817bd1b92 https://doi.org/10.1016/j.jfoodeng.2009.04.001 Zobrazit plný text záznamu Full Text from ScienceDirect