Image segmentation is the process of partitioning an image into meaningful regions or segments. The best methods depend on the task, but some of the most widely used approaches include thresholding, clustering, and deep learning-based techniques. Thresholding is a simple and effective method for separating objects from the background by converting the image to binary format based on pixel intensity. It works well for images with high contrast but may struggle with complex scenes. Clustering methods, such as K-means and watershed segmentation, group pixels into clusters based on similarity. These techniques are effective for dividing an image into distinct regions but can be computationally intensive. Deep learning-based methods, particularly those using Convolutional Neural Networks (CNNs), such as U-Net and Mask R-CNN, are the most powerful and accurate. These models automatically learn features from the data and have been successful in tasks like medical image segmentation and object detection.
What is the best methods for image segmentation?
- How to Pick the Right Vector Database for Your Use Case
- Embedding 101
- Information Retrieval 101
- Getting Started with Milvus
- Vector Database 101: Everything You Need to Know
- 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 is edge AI used in manufacturing for quality control?
Edge AI is increasingly being used in manufacturing for quality control by enabling real-time data processing and analys
How does using a binary embedding (e.g., sign of components only, or learned binary codes) drastically cut down storage, and what kind of search algorithms support such binary vectors?
Using binary embeddings, such as vectors where components are reduced to 0/1 bits or learned binary codes, drastically r
What strategies can be used to update or improve embeddings over time as new data becomes available, and how would that affect ongoing RAG evaluations?
To update embeddings as new data arrives, one effective approach is incremental retraining combined with versioning. Per