کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6540517 158862 2015 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Automatic classification for field crop insects via multiple-task sparse representation and multiple-kernel learning
ترجمه فارسی عنوان
طبقه بندی اتوماتیک برای حشرات حوزه های مختلف از طریق نمایش چندگانه چند ضلعی و یادگیری چند هسته ای
کلمات کلیدی
طبقه بندی حشرات، نمایش چند ضلعی از اشیاء حشرات، یادگیری چند هسته ای، برنامه نویسی انعطاف پذیر،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
Classification of insect species of field crops such as corn, soybeans, wheat, and canola is more difficult than the generic object classification because of high appearance similarity among insect species. To improve the classification accuracy, we develop an insect recognition system using advanced multiple-task sparse representation and multiple-kernel learning (MKL) techniques. As different features of insect images contribute differently to the classification of insect species, the multiple-task sparse representation technique can combine multiple features of insect species to enhance the recognition performance. Instead of using hand-crafted descriptors, our idea of sparse-coding histograms is adopted to represent insect images so that raw features (e.g., color, shape, and texture) can be well quantified. Furthermore, the MKL method is proposed to fuse multiple features effectively. The proposed learning model can be optimized efficiently by jointly optimizing the kernel weights. Experimental results on 24 common pest species of field crops show that our proposed method performs well on the classification of insect species, and outperforms the state-of-the-art methods of the generic insect categorization.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers and Electronics in Agriculture - Volume 119, November 2015, Pages 123-132
نویسندگان
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