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    吴笑鑫, 高良, 闫民, 赵方. 基于多特征融合的花卉种类识别研究[J]. 北京林业大学学报, 2017, 39(4): 86-93. DOI: 10.13332/j.1000-1522.20160367
    引用本文: 吴笑鑫, 高良, 闫民, 赵方. 基于多特征融合的花卉种类识别研究[J]. 北京林业大学学报, 2017, 39(4): 86-93. DOI: 10.13332/j.1000-1522.20160367
    WU Xiao-xin, GAO Liang, YAN Min, ZHAO Fang. Flower species recognition based on fusion of multiple features[J]. Journal of Beijing Forestry University, 2017, 39(4): 86-93. DOI: 10.13332/j.1000-1522.20160367
    Citation: WU Xiao-xin, GAO Liang, YAN Min, ZHAO Fang. Flower species recognition based on fusion of multiple features[J]. Journal of Beijing Forestry University, 2017, 39(4): 86-93. DOI: 10.13332/j.1000-1522.20160367

    基于多特征融合的花卉种类识别研究

    Flower species recognition based on fusion of multiple features

    • 摘要: 花卉种类识别作为植物自动分类识别的重要分支,有着很高的研究和应用价值。针对当前花卉特征描述存在的局限和花卉识别准确率较低的实际情况,以花卉图像为研究对象,首先对复杂背景图像采用基于显著性检测的Grab Cut分割算法进行预处理,得到单一背景图像;然后在提取花卉图像花冠(所有花瓣)颜色和形状特征的基础上,创新性地提取花蕊区域的颜色和形状所包含的特征信息,并将提取到的18个特征融合成单一特征向量。以支持向量机(SVM)算法为基础构建分类器,通过实验确定核函数与最佳参数;对360幅自建花卉样本库(24个种类,每个种类15幅)进行训练和测试,其中240幅作为训练样本,120幅作为测试样本,并与基于不同特征组合的识别方法进行比较。结果表明:本文提出的基于多特征融合的识别方法具有较高的识别准确率,识别率可以达到92.50%。对通用花卉样本库Oxford 17 flower进行训练与测试,选取其中340幅作为训练样本,170幅作为测试样本,取得了较好的识别效果,验证了本文方法的有效性。

       

      Abstract: As an important branch of plant automatic classification and recognition, flower species recognition is of great value in academic research and practical application. In view of the limitation of description methods for flower features and the problem of low accuracy of flower species recognition, flower images were used as recognition objects, the segmentation algorithm of Grab Cut based on saliency detection was used to preprocess the images with complex background and the images with single background were got. Then the color and shape features of the flower region were extracted. In addition to the color and shape features of the whole flower region, the color and shape features of the pistil/stamen area were also used to represent the flower characteristics more precisely. At last the 18 features were fused into a single feature vector and the SVM (support vector machine) was used for classification and recognition. Experiment based on self-built flower library was conducted for 360 images (15 images each for 24 different species), 240 images were used for the training samples, and the rest of 120 images were used for the test samples. Compared with the recognition methods based on the combination of different features, the method provided in this paper has achieved better recognition effect.The experimental results showed that the recognition accuracy reached 92.50%. Experiments based on Oxford 17 flower library were conducted for 510 images, in which 340 images were used for the training samples, and 170 images were used for the test samples. The experimental results show that the proposed method is effective.

       

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