Which visual features are commonly extracted from video data for search?

When building video search systems, developers commonly extract two categories of visual features from video data: low-level spatial features and high-level temporal features. These features enable content-based matching between user queries and video content through mathematical representations[1][2].

1. Low-level spatial features capture basic visual patterns from individual frames:

• Color features like histograms or dominant color distributions help identify scenes with specific color schemes (e.g., “sunset” videos)
• Texture features using methods like Gabor filters or Local Binary Patterns (LBP) distinguish surfaces like grass, water, or fabric
• Shape descriptors such as edge detection (Canny, Sobel) or contour analysis detect objects with distinct outlines These features are typically extracted from keyframes representing significant visual content changes[2]. For example, a histogram comparing red/orange color distributions could help find beach sunset videos[1].

2. High-level temporal features model motion and time-based relationships:

• Optical flow tracks pixel movement between consecutive frames to detect actions like walking or object rotation
• Motion trajectories plot the path of moving objects across frames
• Spatiotemporal features using 3D CNNs capture combined spatial and motion patterns (e.g., “person opening door”)[2] These features help distinguish videos with similar static frames but different motion patterns, like differentiating between a car accelerating vs braking.

Developers often combine these visual features with other metadata (audio, text descriptions) for better search accuracy. Implementation typically involves:

• Keyframe extraction to reduce processing load
• Feature encoding (e.g., converting color histograms to 128-dim vectors)
• Indexing with ANN libraries like FAISS for efficient similarity search[1][2]. Modern systems might use pre-trained vision models (ResNet, ViT) to extract semantic-aware features, though this introduces higher computational costs.