The best algorithm for feature extraction depends on the application. Traditional methods like SIFT (Scale-Invariant Feature Transform) and HOG (Histogram of Oriented Gradients) are effective for tasks requiring handcrafted features, such as image matching or object detection in low-data scenarios. For deep learning applications, convolutional neural networks (CNNs) are the most effective, as they automatically learn hierarchical features from raw images. Pre-trained models like ResNet, EfficientNet, and Vision Transformers (ViTs) excel in feature extraction, particularly for large-scale datasets.
Which is the best algorithm for feature extraction in images?

- Advanced Techniques in Vector Database Management
- AI & Machine Learning
- Embedding 101
- Natural Language Processing (NLP) Basics
- Getting Started with Zilliz Cloud
- 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 do TTS systems manage code-switching within the same sentence?
Text-to-speech (TTS) systems manage code-switching—switching between languages in a single sentence—through a combinatio
What is the difference between a directed and an undirected graph?
A directed graph and an undirected graph are two fundamental data structures used in computer science to represent relat
How do facial recognition systems work?
Facial recognition systems work by capturing an image, detecting faces, and comparing them to stored templates. Detectio