Case Study 2: Building a Video Summarization Pipeline

Overview

Long videos contain large amounts of redundant information. A video summarization system identifies the most important moments and creates a concise representation — either as a set of keyframes (static summary) or a shorter video clip (dynamic summary). In this case study, you will build a complete video summarization pipeline that combines visual feature extraction, clustering, keyframe selection, and optional captioning to produce informative summaries of long videos.

Problem Statement

Build a video summarization system that: 1. Processes videos of 5-30 minutes in length 2. Extracts visually diverse and informative keyframes 3. Generates captions for each keyframe 4. Produces a structured text summary of the video content 5. Supports configurable summary length (number of keyframes)

Approach

Step 1: Frame Extraction and Feature Computation

Rather than processing every frame, we sample at a fixed rate and extract features:

1. Temporal sampling: Extract frames at 1 FPS (sufficient for most content; adjust for fast-paced videos)
2. Feature extraction: Use CLIP ViT-B/16 to encode each frame, producing a 512-dimensional embedding per frame
3. Scene change detection: Compute cosine similarity between consecutive frame embeddings. Mark potential scene boundaries where similarity drops below a threshold (e.g., 0.85)

Step 2: Keyframe Selection via Clustering

We use K-Means clustering on the frame embeddings to identify diverse visual content:

1. Determine K: The number of keyframes is either user-specified or automatically determined as $K = \max(5, \min(20, \lfloor \text{duration} / 60 \rfloor \times 3))$ (roughly 3 keyframes per minute, bounded between 5 and 20)
2. Clustering: Apply K-Means with K clusters on L2-normalized CLIP embeddings
3. Keyframe selection: For each cluster, select the frame closest to the centroid (most representative) that is also temporally distinct from already-selected keyframes (minimum 10 seconds apart)
4. Temporal ordering: Sort selected keyframes by timestamp

Step 3: Alternative Selection Method — Submodular Maximization

For higher quality summaries, we implement a submodular objective that balances representativeness and diversity:

$$S^* = \arg\max_{|S| \leq K} \left[\lambda \sum_{i \in V} \max_{j \in S} \text{sim}(i, j) + (1 - \lambda) \sum_{i \in S} \sum_{j \in S, j \neq i} (1 - \text{sim}(i, j))\right]$$

The first term (representativeness) ensures every frame in the video has a similar frame in the summary. The second term (diversity) ensures selected frames are different from each other. We solve this with a greedy algorithm (which guarantees a $(1 - 1/e)$ approximation for submodular functions).

Step 4: Caption Generation

For each keyframe, generate a descriptive caption using BLIP-2:

1. Load the BLIP-2 model (OPT-2.7B variant)
2. Generate captions for each keyframe with beam search
3. Optionally generate temporal context captions: "At [timestamp], [caption]"

Step 5: Structured Summary Generation

Combine keyframes and captions into a structured summary:

1. Group keyframes into segments based on visual similarity and temporal proximity
2. Generate a segment title using the dominant visual theme
3. Create a timeline view: ordered list of (timestamp, keyframe, caption) tuples
4. Generate an overall summary using the language model, conditioned on all captions

Implementation Details

Scene Change Detection Algorithm

For each pair of consecutive frames (f_t, f_{t+1}):
    sim = cosine_similarity(clip_embed(f_t), clip_embed(f_{t+1}))
    if sim < threshold:
        mark t as a scene boundary

Scene boundaries help ensure keyframes are selected from different scenes rather than clustering within visually similar segments.

Handling Diverse Video Types

The system adapts to different content types:

• Lectures/Presentations: High visual similarity (static slides). Use scene change detection to identify slide transitions; select one keyframe per slide.
• Sports/Action: Rapid visual changes. Increase sampling rate to 2 FPS and use more clusters.
• Nature/Documentary: Gradual scene changes. Standard 1 FPS sampling with moderate cluster count.
• Tutorials: Mix of talking head and demonstrations. Prioritize frames showing demonstrations over talking head segments.

Quality Filtering

Remove low-quality keyframes: - Blur detection: Compute Laplacian variance; reject frames below threshold - Black/white frames: Reject frames with very low or very high mean pixel intensity - Transition frames: Reject frames at exact scene boundaries (often contain fade/dissolve artifacts)

Results

Evaluation on TVSum Dataset

We evaluate on the TVSum benchmark (50 YouTube videos with human importance annotations):

Example Output (5-minute cooking video)

Keyframes selected: 8 frames 1. [0:05] "A person standing in a kitchen with various ingredients on the counter" 2. [0:32] "Hands chopping vegetables on a wooden cutting board" 3. [1:15] "A large pot of water boiling on the stove" 4. [1:48] "Adding pasta to a pot of boiling water" 5. [2:35] "Stirring a red sauce in a pan with a wooden spoon" 6. [3:20] "Grating cheese over a bowl" 7. [4:10] "Draining pasta in a colander in the sink" 8. [4:50] "A finished plate of pasta with red sauce and grated cheese"

Overall summary: "This video shows a person preparing a pasta dish. The process includes chopping vegetables, boiling water, cooking pasta, preparing a red sauce, and assembling the final dish with grated cheese."

Key Lessons

1. CLIP features are excellent for video summarization: The pre-trained visual-semantic alignment means that visually similar frames cluster together regardless of low-level appearance variations (lighting, angle).

2. Clustering outperforms uniform sampling significantly: K-Means ensures diversity by selecting frames from different visual clusters, while uniform sampling may oversample redundant content (e.g., multiple frames of the same scene).

3. Submodular optimization provides marginal but consistent improvement: The explicit diversity-representativeness tradeoff (controlled by lambda) produces more balanced summaries than K-Means alone, but the improvement is modest (3% F1).

4. Caption quality correlates with keyframe quality: Frames that produce informative, specific captions (as opposed to generic "a person standing") tend to be more important. This can be used as an additional quality signal.

5. Adaptivity is essential: No single set of parameters works for all video types. The summarization system should adjust sampling rate, cluster count, and selection criteria based on the detected content type.

6. Temporal constraints prevent redundancy: Without the minimum time gap between selected keyframes, the algorithm may select multiple frames from the same interesting scene, missing other parts of the video entirely.

Extensions

• Add audio analysis to detect speech segments and prioritize frames during narrated explanations
• Implement dynamic video summaries (selecting short clips instead of frames)
• Build a user-interactive summarization tool where users can specify interests ("focus on cooking techniques")
• Integrate with a video-language model for query-based summarization ("summarize the parts about seasoning")

Code Reference

The complete implementation is available in code/case-study-code.py.