User-provided sketches or images can serve as effective video queries by leveraging visual similarity matching and feature extraction techniques. This approach allows developers to build systems where users input a reference image or drawing, and the system retrieves videos containing visually similar content. The process typically involves three stages: feature extraction from the input, comparison with video frames, and ranking results based on similarity metrics[4][5][7].
First, the system converts the sketch or image into a numerical representation using computer vision techniques. For sketches, edge detection algorithms like Canny edge or Hough transforms help identify key shapes and lines. For photographs, convolutional neural networks (CNNs) such as ResNet-50 extract high-level visual features. These features are stored as vectors (e.g., 512-dimensional arrays) that capture essential visual characteristics. Videos are preprocessed by extracting keyframes at regular intervals (e.g., 1 frame/second) and converting them into similar feature vectors[5][7].
Developers can implement this using open-source tools like OpenCV for basic image processing or TensorFlow/PyTorch for deep learning-based feature extraction. For example, a Python script using OpenCV might:
- Resize the input sketch to 224x224 pixels
- Apply grayscale conversion and edge detection
- Use a pre-trained CNN to generate feature embeddings
- Compare these embeddings against a pre-indexed video database using cosine similarity
The system then returns video segments with the highest similarity scores. Practical applications include finding merchandise in video shopping platforms using product sketches, or locating specific scenes in film archives using rough storyboard drawings[4][5].
Key challenges include handling varying drawing styles and optimizing for real-time performance. Solutions might involve data augmentation during model training (e.g., adding noise to sketches) and using approximate nearest neighbor algorithms like FAISS for faster similarity searches. Current implementations show mean average precision (mAP) scores around 0.78-0.85 for sketch-to-video retrieval in controlled benchmarks[7].