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The National Agricultural Library is one of four national libraries of the United States, with locations in Beltsville, Maryland and Washington, D.C. It houses one of the world's largest and most accessible agricultural information collections and serves as the nexus for a national network of state land-grant and U.S. Department of Agriculture field libraries. In fiscal year 2011 (Oct 2010 through Sept 2011) NAL delivered more than 100 million direct customer service transactions.

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Journal Article

Journal Article

Fish species classification by color, texture and multi-class support vector machine using computer vision  [2012]

Hu, Jing; Li, Daoliang; Duan, Qingling; Han, Yueqi; et al.

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Remote diagnose of fish diseases for farmers is unrealized in China, but use of mobile phones and remote analysis based on image processing can be feasible due to the widespread use of mobile phones with camera features in rural areas. This paper presents a novel method of classifying species of fish based on color and texture features and using a multi-class support vector machine (MSVM). Fish images were acquired and sent by smartphone, and the method utilized was comprised of the following stages. Color and texture subimages of fish skin were obtained from original images. Color features, statistical texture features and wavelet-based texture features of the color and texture subimages were extracted, and six groups of feature vectors were composed. LIBSVM software was tested using leave-one-out cross validation to find the best group for classification in feature selection procedure. Two multi-class support vector machines based on a one-against-one algorithm were constructed for classification. The feature selection results showed that the Bior4.4 wavelet filter in HSV color space achieved greater accuracy than the other feature groups. The classification results indicate that only the DAGMSVM meets the requirement of time efficiency for the system. The results of this study suggest that the best classification model for fish species recognition is composed of a wavelet domain feature extractor with Bior4.4 wavelet filter in HSV color space and a one-
against-one algorithm based DAGMSVM classifier.
From the journal
Computers and electronics in agriculture
ISSN : 0168-1699

Bibliographic information

Language:
English
Type:
Journal Article
In AGRIS since:
2014
Volume:
88
Start Page:
133
End Page:
140
Publisher:
Elsevier B.V.
All titles:
"Fish species classification by color, texture and multi-class support vector machine using computer vision"@eng
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Bibliographic information

Language:
English
Type:
Journal Article
In AGRIS since:
2014
Volume:
88
Start Page:
133
End Page:
140
Publisher:
Elsevier B.V.
All titles:
"Fish species classification by color, texture and multi-class support vector machine using computer vision"@eng