基于多角度多区域特征融合的苹果分类方法

日常生活中人们分拣辨别不同种类的苹果需要消耗大量的人力物力，为解决这一问题，提出了一种基于多角度多区域特征融合的苹果图像分类方法。首先，收集五类总共329个苹果，使用手机摄像头从上面、下面和3个不同侧面共五个角度采集每个苹果的图像，每个图像裁剪若干个（1~9）区域块；其次，每个区域块用颜色直方图向量来表示，多个区域块的直方图向量通过首尾相连进行融合，以此生成一个图像的表示；最后，将得到的329个样本数据用12种分类器进行分类比较。实验结果表明，当多角度多区域图像特征融合时，分类效果总是好于单角度单区域，而且越多越好；当使用5个角度的图像，每个图像裁剪9个区域时，偏最小二乘（PLS）分类器的分类精度达到97.87%，好于深度学习。所提方法操作简单、精度较高，算法复杂度为4n，n为图像裁剪区域块总数，可以推广成手机应用，并应用到更多水果和植物图像分类上。

Multi-perspective multi-region feature fusion for apple classification

Since manual sorting of apples is a huge project in our daily life, an apple image classification approach based on multi-perspective multi-region feature fusion was proposed. First of all, five classes of apples, containing 329 in total, were collected. For each apple, five images from five different perspectives were obtained:top, bottom, side1, side2 and side3. From each image, several (one to nine) small image regions were cut. Secondly, each region block was represented by color histogram vector, and the histogram vectors of region blocks were fused together end to end to generate a representation of the image. Finally, 12 classifiers were used to classify 329 samples. The experimental results show that the multi-perspective multi-region based method significantly outperforms single-perspective single-region based method, and the more the number of perspectives/regions, the better the result. In particular, classification performance reaches 97.87% by PLS (Partial Least Squares) even better than deep learning when using nine regions for each image cropped at five angles. The method is easy but efficient, whose computation complexity is 4n, where n is the total number of blocks in image cropping area. Thus, it can be applied to mobile applications and applied to more fruit and plant image classification.