How to learn Clustering Image Collections for Feature Extraction And Embeddings in Computer Vision Engineer for free

Why this matters

Clustering image collections turns raw embeddings into meaningful groups without labels. As a Computer Vision Engineer, you will use clustering to:

Deduplicate and group near-identical products or shots, reducing catalog noise.
Create semi-automatic labels from clusters to accelerate supervised training.
Discover structure in large datasets (e.g., scenes, styles, species) for analytics.
Build retrieval experiences: cluster centers as anchors for browsing or search.
Clean datasets by flagging outliers and mislabeled items.

Concept explained simply

Each image is turned into a vector (embedding) by a vision model. Images that look alike have vectors that point in similar directions and are close by under a distance metric (often cosine or Euclidean). Clustering groups these vectors so each group represents a visual concept or identity.

Mental model

Imagine a star map. Each image is a star. Constellations are clusters: stars close to one another form a recognizable shape. You can find constellations by measuring distances and grouping stars that are close. Some stars are alone (outliers) and should not be forced into a constellation.

Practical workflow

Get embeddings: Use a consistent vision model (e.g., generic image model or task-specific model like a face encoder). Keep model and preprocessing fixed.
Normalize: L2-normalize embeddings so length is 1. This stabilizes cosine distance and many algorithms.
Reduce dimensions (optional but helpful): PCA to 50–256 dims removes noise and speeds clustering. Keep variance around 90–95% as a starting point.
Choose a distance: Cosine for normalized embeddings; Euclidean is fine too. Be consistent across steps.
Select an algorithm: Pick based on data shape, noise, and whether you know K (the number of clusters).
Tune & run: Adjust parameters (e.g., K for k-means, eps/min_samples for DBSCAN, min_cluster_size for HDBSCAN).
Evaluate: Internal metrics (silhouette, Davies–Bouldin) + manual inspection samples. Iterate.
Use outputs: Save cluster labels, medoids/centroids, and outlier lists for downstream tasks.

Choosing an algorithm

K-Means (and Spherical K-Means)

Use when: Clusters are roughly spherical, sizes similar, you can guess K.
Notes: Works well on L2-normalized embeddings with cosine/spherical variant. Needs K.
Key params: K, init restarts (increase for stability), max iterations.
Pitfalls: Sensitive to outliers and imbalanced clusters.

Agglomerative (Hierarchical) Clustering

Use when: You want a hierarchy or do not assume spherical clusters.
Notes: Linkages: average/complete (cosine-friendly), ward (Euclidean only).
Key params: Linkage type, distance threshold or number of clusters.
Pitfalls: Can be slower on very large datasets; choose fast distance computations.

DBSCAN

Use when: You want to find clusters of varying shape and mark noise automatically; K is unknown.
Key params: eps (neighborhood radius), min_samples (density requirement).
Tuning tip: Plot k-distance curve to pick eps at the knee; start with min_samples around dimensionality.
Pitfalls: Single global eps can be hard to set for datasets with varying densities.

HDBSCAN

Use when: You need DBSCAN-like noise handling but with varying densities.
Key params: min_cluster_size (smallest group you care about), min_samples (robustness).
Output: Cluster labels + probabilities; some points become -1 (noise).

Spectral Clustering

Use when: Non-convex clusters and smaller datasets where building a similarity graph is feasible.
Notes: Requires choosing K and building a similarity matrix; can be expensive for large N.

Preprocessing and distance choices

L2-normalize embeddings: Makes cosine distance meaningful; often improves stability.
Dimensionality reduction: PCA to 50–256 dims reduces noise; keep a small validation set to ensure separability is preserved.
Metric: Cosine distance is common for normalized embeddings; Euclidean often works similarly post-normalization.
Whitening (optional): Helpful if some dimensions dominate; validate with a small metric check before/after.

Worked examples

Example 1: Retail catalog dedup and variants

Goal: Group near-duplicates and color variants of the same product.

Embeddings: 2048-D image encoder, L2-normalized.
Reduce: PCA to 128 dims.
Algorithm: Spherical k-means with cosine distance.
Choosing K: Try K in {100, 150, 200}; pick the best silhouette and check 30 random clusters manually.

Outcome: K=150 had the best mix of compactness and interpretable groups. Outliers were flagged by high distance to centroids; reviewed manually for data cleaning.

Example 2: Wildlife camera traps (many outliers)

Goal: Discover species and discard noise (empty frames, motion blur).

Embeddings: 512-D model, normalized; PCA to 64 dims.
Algorithm: HDBSCAN with min_cluster_size=15, min_samples=5.
Evaluation: 50 sampled images per cluster for visual consistency; track % noise points.

Outcome: Clear species groups emerged; 22% points labeled noise—useful for automatic filtering.

Example 3: Grouping event photos by person (face clustering)

Goal: Group photos of the same person.

Embeddings: 128-D face embeddings; L2-normalized.
Algorithm: Agglomerative (average linkage) with cosine distance; cut tree by distance threshold (e.g., 0.35–0.45).
Validation: Pairwise precision/recall on a small labeled subset.

Outcome: Good identity clusters; a conservative threshold improved precision and avoided merges of look-alikes.

Evaluating clusters

Internal metrics: Silhouette score (closer to 1 is better), Davies–Bouldin (lower is better), Calinski–Harabasz (higher is better).
Practical checks:
- Sample 20–50 images from several clusters; verify a coherent theme or identity.
- Check cluster size distribution; extremely large clusters can hide merges, many singletons can mean over-segmentation.
- Inspect top-k nearest neighbors to cluster centroids/medoids; look for off-topic items.
Human-in-the-loop: Let users merge/split clusters and accept/reject outliers; feed corrections back into your process.

Practical tips

Standardize pipeline: same model, preprocessing, and normalization across all images.
Batch effects: If images come from different sources, check for source-driven clusters; mitigate with normalization or domain adaptation.
Speed: Use PCA and mini-batch variants (where available). For very large N, cluster a sample, then assign the rest to nearest centroids.
Imbalanced data: Prefer HDBSCAN or agglomerative when small/large clusters coexist; k-means may over-merge small groups.
Threshold-based assignment: For identity tasks, use a max distance to centroid/medoid; leave far points unassigned.

Exercises (hands-on)

Work through these. A quick test is available to everyone; only logged-in users have their progress saved.

Exercise 1: Pick K from silhouette scores

You clustered the same embeddings with different K and got:

K=2 → silhouette=0.41
K=3 → silhouette=0.52
K=4 → silhouette=0.47

Choose the best K and explain why. Then list two follow-up checks you would do before finalizing.

Exercise 2: Many outliers, uneven clusters

Dataset: 10,000 images, normalized 512-D embeddings. Expect many singletons and a few medium groups. Propose a clustering approach (algorithm + key parameters) that handles noise gracefully, and describe how you will evaluate the result.

Self-check checklist

Did you justify the metric (cosine vs Euclidean) consistently with normalization?
Did you propose at least one internal metric for evaluation?
Did you plan a manual inspection sample?
Did you address outliers/noise explicitly?

Common mistakes and how to self-check

Skipping normalization: Self-check: compute average vector norm; if not ~1, L2-normalize and re-run.
Using Euclidean on unnormalized embeddings: Self-check: compare cosine vs Euclidean silhouette; pick the better one after normalization.
Forcing K when K is unknown: Self-check: try density-based methods; compare cluster size histograms.
Ignoring outliers: Self-check: measure % of points far from centroids; review samples.
No manual validation: Self-check: visually inspect at least 20 clusters; flag inconsistent ones.
Over-reducing dimensions: Self-check: after PCA, verify that nearest neighbors remain mostly unchanged on a validation subset.

Who this is for

Computer Vision Engineers organizing large unlabeled image sets.
ML practitioners building retrieval, deduplication, or semi-supervised pipelines.

Prerequisites

Basic understanding of embeddings and distance metrics.
Ability to run inference to compute image embeddings.
Familiarity with at least one clustering algorithm.

Learning path

Compute and normalize embeddings for a small image set (500–2,000 images).
Run PCA and compare clustering metrics before/after.
Try k-means vs HDBSCAN; compare cluster size distributions and manual samples.
Design a simple UI (even a notebook grid) to view cluster thumbnails and annotate merges/splits.

Practical projects

Visual Deduplicator: Cluster product images, mark clusters with near-duplicate pairs, export a CSV of suggested merges.
Species Discovery: Cluster wildlife photos, tag noise, and create a shortlist of candidate species clusters for expert review.
Event Photo Grouper: Cluster faces by identity with a distance threshold; build a small viewer to browse groups.

Next steps

Integrate clustering into your data curation workflow (cleaning, dedup, semi-labeling).
Track metrics over time as new images arrive; schedule periodic re-clustering or incremental assignment.
Connect cluster labels to downstream training to boost supervised models.

Mini challenge

You have 50,000 normalized embeddings with mixed densities. Design a three-pass pipeline that first removes extreme outliers, then finds stable cores, then assigns leftovers to the nearest cluster if within a threshold. Write the steps and key parameters you would start with.

Clustering Image Collections

Table of Contents