基于点数量与尺寸的点阵图像数字分类：CNN替代机器学习方案咨询

Hey there! Since your images don’t have the typical edge or texture features CNNs excel at extracting, there are several straightforward, effective alternatives tailored to your task—where the target value is directly tied to the number and size of points in the image. Here are the best options, optimized for both raw and binary black-and-white inputs:

1. Traditional ML Models + Handcrafted Feature Engineering #

This is your best bet because your task’s key features are explicit (point count and size) — no need for a model to guess them. Here’s how to implement it:

• Preprocessing: If using raw images, first convert them to binary (thresholding) to isolate points from the background. Binary inputs will make this step way cleaner and more reliable.
• Feature Extraction:
  • Use connected component analysis (e.g., cv2.connectedComponents() in OpenCV) to count the number of distinct points.
  • Calculate the area of each connected component to get individual point sizes, then derive aggregate metrics like average point size, total point area, or the 90th percentile of point sizes.
• Model Selection:
  • For regression (since you need a numerical output), go with Random Forest Regression, XGBoost/LightGBM Regression, or even simple Linear Regression (if the relationship between features and target is linear). These models work great with structured numerical features, train fast, and are easy to interpret.
  • If your "categories" are discrete, swap in classification variants like Random Forest Classifier or SVC.

2. Density-Based Regression #

If your points are relatively uniformly distributed, you can skip counting individual points entirely:

• Calculate the pixel density of the foreground (points) — divide the total number of white pixels by the total image pixels. This metric naturally combines both point count and size (larger points contribute more pixels).
• Feed this single density value (or a few regional density values if points cluster) into a regression model like those mentioned above. This is a super lightweight option for simpler datasets.

3. Multi-Layer Perceptrons (MLPs) #

While this is still deep learning, it’s not a CNN — perfect if you want to avoid CNNs but still leverage automated feature learning:

• Flatten your image into a 1D vector (e.g., a 256x256 binary image becomes a 65536-element array of 0s and 1s).
• Pass this vector through a small MLP (2-3 hidden layers) trained for regression. Note: This works best if your images are low-resolution; high-res inputs will lead to huge input dimensions that are inefficient to train on. For your task, handcrafted features will almost always outperform an MLP.

4. Support Vector Machines (SVMs) #

SVMs shine for small-to-medium datasets with low-dimensional features:

• Use the same handcrafted features (point count, average size, etc.) as in option 1.
• Train an SVR (Support Vector Regression) model to predict the target numerical value. SVMs often have strong generalization performance when feature dimensions are low.

Pro Tips #

• Prioritize binary inputs: They eliminate noise from raw images and make connected component analysis far more reliable.
• Handle overlapping points: If points overlap, use morphological operations like opening (erosion followed by dilation) to separate them before counting.
• Start simple: Test linear regression or random forest with basic features first — you might find it already meets your performance needs without overcomplicating things.

内容的提问来源于stack exchange，提问作者Max