The best method for feature extraction depends on the specific application and dataset. Classical methods like Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), and Speeded-Up Robust Features (SURF) are effective for detecting edges, textures, and shapes in images. These methods work well for traditional applications like object tracking and image matching. For more complex tasks, deep learning-based methods, such as convolutional neural networks (CNNs), are widely used. Layers in CNNs automatically learn hierarchical features from raw pixel data, making them highly effective for tasks like classification and object detection. Pre-trained models like VGG, ResNet, and EfficientNet can be fine-tuned for specific feature extraction needs. Additionally, attention-based models like Vision Transformers (ViT) have gained popularity for their ability to capture global relationships in images. Combining classical and deep learning methods can sometimes yield the best results, especially in hybrid workflows.
What are best method for feature extraction in image?

- Retrieval Augmented Generation (RAG) 101
- AI & Machine Learning
- GenAI Ecosystem
- Getting Started with Milvus
- Embedding 101
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
How does DeepSeek achieve high performance with lower computational costs?
DeepSeek achieves high performance with lower computational costs primarily through its efficient architecture and optim
How do augmentation policies work for reinforcement learning?
Augmentation policies in reinforcement learning (RL) refer to techniques used to expand or enhance the training data to
How do quantum computers solve linear systems of equations?
Quantum computers solve linear systems of equations using specialized algorithms that leverage quantum properties like s