改进图像像素分类的方法
这是我们试图解决的问题：
- 目标是将彩色图像的像素分为 3 个不同的类别。
- 我们有一组用于训练目的的手动分类数据，
- 像素几乎彼此不相关（每个像素都有单独的行为） - 因此最有可能的分类是针对每个单独的像素并基于其单独的特征。
- 大约 3 个类别可以映射到红色、黄色和黑色颜色系列的颜色。
- 我们需要让系统半自动，即 3 个参数来控制 3 个结果出现的概率（用于最终的微调）
考虑到这一点：
- 您会选择哪种分类技术？
- 您将使用哪些像素特征进行分类（RGB、Ycc、HSV 等）？
- 您将选择哪些修改函数来在三种结果之间进行良好调整。
我的第一次尝试是基于
- 朴素贝叶斯分类器
- HSV（也尝试过 RGB 和 Ycc）
- （未能找到合适的调整函数）
有什么建议吗？ 谢谢
Here is the problem we are trying to solve:
- Goal is to classify pixels of a colored image into 3 different classes.
- We have a set of manually classified data for training purposes
- Pixels almost do not correlate to each other (each have individual behaviour) - so most likely classification is on each individual pixel and based on it's individual features.
- 3 classes approximately can be mapped to colors of RED, YELLOW and BLACK color families.
- We need to have the system semi-automatic, i.e. 3 parameters to control the probability of the presence of 3 outcomes (for final well-tuning)
Having this in mind:
- Which classification technique will you choose?
- What pixel features will you use for classification (RGB, Ycc, HSV, etc) ?
- What modification functions will you choose for well-tuning between three outcomes.
My first try was based on
- Naive bayes classifier
- HSV (also tried RGB and Ycc)
- (failed to find a proper functions for well-tuning)
Any suggestion?
Thanks
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对于图像中的每个像素，尝试使用该像素周围的 n x n 窗口的颜色直方图作为其特征。对于不同照明条件下的通用颜色匹配，我很幸运地使用了色调和饱和度的二维直方图，并且每个维度上的箱数相对较少。根据您的照明一致性，直接使用 RGB 值可能会有意义。
对于分类器，手动调整要求最容易使用类权重来表达：指定假阴性与假阳性相对成本的参数。我只在 SVM 中使用过此功能，但我确信您可以找到支持类似概念的其他分类器的实现。
For each pixel in the image try using the histogram of colors the n x n window around that pixel as its features. For general-purpose color matching under varied lighting conditions, I have had good luck with using two-dimensional histograms of hue and saturation with a relatively small number of bins along each dimension. Depending upon your lighting consistency it might make sense for you to directly use the RGB values.
As for the classifier, the manual-tuning requirement is most easily expressed using class weights: parameters that specify the relative costs of false negatives versus false positives. I have only used this functionality with SVMs, but I'm sure you can find implementations of other classifiers that support a similar concept.