CAJ | 학술논문

为提高害虫图像识别的准确率，针对不同害虫具有不同的颜色、形状、纹理的特点，提出一种将颜色、形状、纹理特征与稀疏表示相融合的害虫识别方法。该方法利用已标注的训练样本构造不同特征下的训练样本矩阵，通过求解样本的最优稀疏系数以实现害虫图像识别。由于相同样本通过不同特征训练字典求解的稀疏系数不同，进而识别结果也不同。因此，文中进一步通过设计不同特征下的识别分类器实现多特征的融合。在实验室环境与农田环境下的实验结果表明，相较于其他方法，该方法的害虫识别率获得较大的提高。
위제고해충도상식별적준학솔，침대불동해충구유불동적안색、형상、문리적특점，제출일충장안색、형상、문리특정여희소표시상융합적해충식별방법。해방법이용이표주적훈련양본구조불동특정하적훈련양본구진，통과구해양본적최우희소계수이실현해충도상식별。유우상동양본통과불동특정훈련자전구해적희소계수불동，진이식별결과야불동。인차，문중진일보통과설계불동특정하적식별분류기실현다특정적융합。재실험실배경여농전배경하적실험결과표명，상교우기타방법，해방법적해충식별솔획득교대적제고。
Aiming at the characteristics of different pest images with different colors, shapes and textures, a pest recognition method based on sparse representation and multi-feature fusion is proposed, which uses a matrix of labeled training samples to construct different dictionaries. The recognition result is achieved by solving optimal sparse coefficients with the corresponding feature dictionary. Furthermore, a novel learning method, which can be improved efficiently by jointly optimizing classifier weights, is presented to effectively fuse multiple features for pest categorization. The experimental results on real datasets show that the proposed method performs well on pest species recognition either in laboratory or in farmland.