Clustering is only available for enterprise customers, email info@mixpeek.com for a demo.

Clusters in Mixpeek are groups of similar features automatically discovered or manually defined. They enable efficient organization, search, and analysis of your multimodal features.

Need to cluster manually against business rules, domain expertise or existing hierarchies? Use Taxonomies

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How Clustering Works

1

Feature Extraction

Assets are processed into features representing:

  • Visual content
  • Objects
  • Spoken Words
  • Metadata
  • etc.
2

Similarity Calculation

Features are compared using:

  • Vector similarity
  • Semantic relationships
  • Temporal proximity
3

Cluster Formation

Similar features are grouped based on:

  • Distance thresholds
  • Density patterns
  • User-defined rules

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Use Cases

Content Organization

Automatically organize video libraries by:

  • Content type
  • Visual similarity
  • Semantic themes

Pattern Discovery

Uncover hidden patterns in your content:

  • Common scenes
  • Recurring themes
  • Related sequences

Search Enhancement

Improve search efficiency through:

  • Cluster-based filtering
  • Contextual recommendations
  • Similar content discovery

Quality Control

Monitor and maintain content quality by:

  • Identifying outliers
  • Detecting anomalies
  • Validating content consistency

​
Implementation

# Create development namespace
POST /namespaces
{
  "namespace": "content_clusters_dev",
  "embedding_modeles": ["text", "image", "multimodal"]
}

# Create collections
POST /collections # with X-Namespace: content_clusters_dev
{
  "collection": "sample"
}

​
Internal Cluster Structure

Features store cluster assignments in a simplified array structure:

{
  "entities": [
    {
      "id": "clu_123",
      "name": "Athletic Footwear Ads",
      "coordinates": {
        "x": -0.45,
        "y": 0.78
      },
      "score": 0.95
    }
  ]
}

​
Searching with Clusters

POST /features/search
{
  "collections": ["sample"],
  "filters": {
    "AND": [
      {
        "key": "entities[].cluster",
        "operator": "eq",
        "value": "Athletic Footwear Ads" # can use cluster name
      }
    ]
  }
}

​
Best Practices for Video Clustering

1

Preprocessing

  • Extract features at appropriate intervals (10-15 seconds recommended)
  • Use the scene detection parameter to identify natural segment boundaries
  • Consider both visual and audio features for complete context
  • Normalize video resolution and quality for consistent processing
2

Model Selection

  • Use multimodal embeddings for combined visual-semantic understanding
  • Consider any of our specialized models for specific content types (sports, ads, etc.)
  • Balance model complexity with processing requirements
  • Test different embedding combinations for optimal results
3

Cluster Configuration

  • Start with conservative clustering parameters (higher min_cluster_size)
  • Adjust confidence thresholds based on content similarity requirements
  • Use appropriate sample sizes for initial cluster discovery
  • Enable automatic naming for better cluster interpretability
4

Performance Optimization

  • Batch process similar video content together
  • Cache frequently accessed cluster assignments
  • Use appropriate indexing strategies for faster lookups
  • Monitor and adjust resource utilization

Video clustering can be resource-intensive. Consider these limitations:

  • Maximum video duration: 4 hours
  • Maximum file size: 2GB
  • Processing timeout: 30 minutes
  • Rate limits apply to clustering requests

For optimal results, combine clustering with taxonomies when dealing with domain-specific video content. This provides both automated discovery and structured organization.

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TaxonomiesAvailable Models