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MindMap Gallery Traditional feature extraction method

Traditional feature extraction method

In the field of image analysis and object detection, traditional feature extraction methods such as LBP, HOG and SIFT combined with modern deep learning frameworks such as PyTorch and TensorFlow have promoted the rapid development of technology. The efficient YOLOv3, SSD and Faster RCNN models were detected from the classic Harris corners, with the single-stage and two-stage models having their own advantages. Machine learning algorithms such as K-mean clustering, KNN and SVM play an important role in image matching, texture classification and face recognition. These methods are widely used in vehicle detection, pedestrian recognition and image stitching, providing a powerful tool for image analysis.

Edited at 2025-03-08 22:20:28

Mason·Carter

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Traditional feature extraction method

Mason·Carter

Recent works View more works>>

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Outline

Traditional feature extraction method

SIFT feature extraction

Scale-invariant feature transformation

Extract key points in images

Key points have scale immutability

Key points have rotational invariance

Describe local image features

Generate key point descriptors

The descriptor is unique

Applied to image matching

For image stitching

For object recognition

Harris corner detection

Corner detection algorithm

Utilize the autocorrelation matrix in the local window

The eigenvalue of the matrix reflects the pixel changes in the window

When the eigenvalue is large, it indicates that the corner point exists

No need to specify the window size

Strong adaptability

Have invariance in image rotation

Applied to image analysis

For image feature extraction

For image registration

HOG (Dimensional Gradient Histogram)

Feature Descriptor

Calculate the local gradient direction of the image

Capture edge information

Reflect local shape information

Build a histogram

Statistical gradient direction distribution

Form eigenvectors

Applied to target detection

Used for pedestrian testing

For vehicle inspection

LBP (local binary mode)

Texture feature extraction

Encode local areas

Comparing neighborhood pixels with central pixels

Generate binary mode

Form a feature histogram

Statistics on the frequency of occurrence of different modes

Constitute image description

Applied to image analysis

For face recognition

For texture classification

Machine Learning Algorithms

SVM support vector machine

Classification Algorithm

Find the best classification hyperplane

Maximize classification intervals

Improve classification accuracy

Nuclear skills deal with nonlinear problems

Mapping data to high-dimensional space through kernel functions

Solve the problem of linear inseparability

Applied to pattern recognition

Used for text classification

for bioinformatics

KNN-Decision Tree (Combination/Comparison)

K nearest neighbor algorithm

Instance-based learning

Decide categories by recent K neighbor votes

Simple and intuitive

Sensitive to distance

Distance metric affects classification results

You need to select the appropriate distance function

Decision tree

Classification and regression trees

Decision-making through a tree structure

Each node represents a property test

Easy to understand and explain

Intuitive results

Can process numerical and category data

Combination and comparison

Combining the advantages of both

KNN's flexibility and explanatory decision tree

Improve the generalization ability of the model

Compare the differences between the two

Computational cost of KNN and complexity of decision tree

Differences in applicable scenarios

KNN

Basic example learning

No explicit learning process required

Make predictions directly based on the labels of nearest neighbors

Simple and easy to implement

Sensitive to data distribution

Uneven data distribution may affect performance

Proper data preprocessing is required

Applied to recommendation systems

For movie recommendations

Used for product recommendations

K-mean clustering

Unsupervised learning algorithm

Divide the data into K clusters

High similarity within the cluster

Low similarity between clusters

Optimization through iterative

Continuously adjusting the cluster center

Until converge

Applied to data mining

Used for market segmentation

For image segmentation

Deep Learning Framework

Target detection: TensorFlow

Open source machine learning library

Developed by Google

Strong community support

Wide application scenarios

Supports multiple deep learning models

Provide rich APIs

Convenient model construction and training

Applied to target detection

Implementing the R-CNN series model

Implementing the YOLO series model

PyTorch

Dynamic Computing Graph Framework

Easy to debug and experiment

The network structure can be modified at runtime

Improve research efficiency

Supports GPU acceleration

Efficient calculations using CUDA

Accelerate the model training process

Applied to target detection

Implementing the SSD model

Implementing the Faster R-CNN model

Object detection model

Two-stage model

R-CNN

Regional convolutional neural network

Extract regional suggestions using selective search

Extract features using CNN

Classification and bounding box regression

Classify suggestions for each region

Precisely predict bounding box position

SPP-Net

Space pyramid pooling network

Introduce the pooling layer of the spatial pyramid

Improve the scale invariance of features

Improve detection speed

Reduce repeated calculations for CNNs

Speed up model inference

Fast R-CNN

Fast regional convolutional neural network

Optimizing the training process of R-CNN

Using multitasking loss function

Improve training efficiency

Shared convolution calculation

Reduce training time

Faster R-CNN

Faster regional convolutional neural networks

Introducing Regional Recommendation Network (RPN)

Implement end-to-end training

Real-time object detection

Significantly improve detection speed

Suitable for real-time applications

Single-stage model (end to end)

YOLOv1

You Only Look Once

Treat object detection as a regression problem

Directly from image pixels to bounding box coordinates and category probability

Real-time object detection

High-speed detection performance

Suitable for real-time systems

SSD

Single-shot detector

Detection in combination with multi-scale features

Improve the accuracy of small object detection

Balance speed and accuracy

It has higher accuracy than YOLOv1

Maintain fast detection speed

YOLOv2

Improved version of YOLO

Introducing Darknet-19 as the basic network

Improves detection accuracy

Improved positioning mechanism

Use logistic regression for target positioning

Reduce positioning errors

YOLOv3

Further development of YOLO

Introducing multi-scale prediction

Improve detection capabilities for small targets

Higher accuracy and robustness

Gain the leading performance on multiple datasets

Detection tasks suitable for complex scenarios