Deduplication of Videos Using Fingerprints, CLIP Embeddings

Video deduplication is a crucial process for managing large-scale video inventory, where duplicates consume storage, increase processing costs, and affect data quality negatively. 

This article explores a robust architecture for deduplication using video segmentation, frame embedding extraction, and clustering techniques. It also highlights key methodologies like video hashing, CLIP embeddings, and temporal alignment for effective deduplication.

Challenges in Video Deduplication

Scale

Video datasets are exponentially larger than images, with each video containing thousands of frames. This presents challenges such as:

• Data volume. Gigabytes to terabytes of data requiring efficient I/O handling.
• Frame explosion. Extracting frames for embedding generation results in millions of data points.

Accuracy

Videos often have slight variations, such as:

• Different resolutions, formats, compression levels, etc.
• Trivial scene changes, like camera movements or overlays, which should not be treated as duplicates.

Latency

Real-time deduplication workflows, such as content moderation, require pipelines that minimize latency while handling massive data volumes.

Architecture

Video Segmentation

The first step in deduplication is segmenting videos into manageable components. We reduce redundant frame comparisons and improve efficiency by identifying scene changes or fixed time intervals.

• Efficiency. Analyzing the entire video frame-by-frame is computationally expensive. Segmentation reduces the workload by focusing on representative frames.
• Focus. Keyframes capture the essence of scenes, improving the accuracy of deduplication.

import cv2

#Video segmentation using scene change detection
video_path = "input_video"
def segment_video(video_path):
    cap = cv2.VideoCapture(video_path)
    frame_count = 0
    segments = []

    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break

        #Detect scene change (compare histograms)
        if frame_count % 30 == 0:  #Process every 30th frame - Can be tuned
            segments.append(frame)
        frame_count += 1

    cap.release()
    return segments

segments = segment_video(video_path)

This implementation showcases a histogram-based segmentation approach, but advanced methods like deep learning-based scene detection can provide better accuracy at the cost of high compute.

Frame Embedding Extraction

After segmentation, representative frames are converted into embeddings using CLIP. These embeddings capture semantic features for similarity comparison.

 Why CLIP?

• Cross-modal understanding. CLIP embeddings excel at capturing semantic relationships across modalities, making them ideal for complex data, such as videos.
• Efficiency. Pre-trained models provide high-quality embeddings without extensive training.

from transformers import CLIPProcessor, CLIPModel
import torch

#Load pre-trained CLIP model
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").cuda()
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

def extract_frame_embeddings(frames):
    inputs = processor(images=frames, return_tensors="pt", padding=True).to("cuda")
    with torch.no_grad():
        embeddings = model.get_image_features(inputs)
    return embeddings.cpu().numpy()

frame_embeddings = extract_frame_embeddings(segments)

CUDA acceleration ensures that large batches of frames are processed efficiently, thus enabling high throughput pipelines.

Temporal Alignment for Embedding Comparison

Temporal alignment involves matching embeddings from different videos to identify duplicates. By aligning embeddings based on timestamps, we ensure that comparisons are meaningful.

 Why Temporal Alignment?

• Context preservation. Aligning embeddings ensures that comparisons account for video timelines, reducing false positives.
• Scalability. By focusing on aligned frames, computational requirements are minimized.

import numpy as np

def temporal_alignment(embeddings_a, embeddings_b, threshold=0.8):
    aligned_pairs = []
    for i, emb_a in enumerate(embeddings_a):
        for j, emb_b in enumerate(embeddings_b):
            similarity = np.dot(emb_a, emb_b) / (np.linalg.norm(emb_a) * np.linalg.norm(emb_b))
            if similarity > threshold:
                aligned_pairs.append((i, j, similarity))
    return aligned_pairs

aligned_pairs = temporal_alignment(frame_embeddings, frame_embeddings)

This implementation uses cosine similarity-based alignment. Advanced methods can incorporate dynamic time warping for non-linear alignments.

Clustering for Deduplication

Clustering groups similar embeddings into clusters and identifies duplicates across videos.

• Scalability. Clustering reduces computational overhead by summarizing similarity scores into groups.
• Flexibility. Techniques like DBSCAN dynamically adapt to clusters of varying densities.

from sklearn.cluster import DBSCAN

#Clustering with DBSCAN
clustering = DBSCAN(eps=0.5, min_samples=5, metric='cosine').fit(frame_embeddings)

#Cluster assignments
cluster_labels = clustering.labels_
for frame, label in zip(segments, cluster_labels):
    print(f"Frame belongs to cluster {label}")

DBSCAN is preferred for its ability to handle noisy data and adapt to non-spherical cluster shapes. HDBSCAN can also be used if the compute permits.

Techniques for Enhanced Deduplication

Video Hashing

Video hashing generates unique signatures for videos, enabling quick deduplication. Techniques like perceptual video hashing consider temporal features for improved accuracy.

from moviepy.editor import VideoFileClip
from imagehash import phash

#Generate a perceptual hash for a video
video = VideoFileClip(video_path)
frame_hashes = [phash(frame.to_image()) for frame in video.iter_frames()]  
hash_signature = ''.join(map(str, frame_hashes))
print("Video Hash Signature:", hash_signature)

Combining Temporal Alignment With Clustering

Integrating temporal alignment with clustering improves precision by filtering outliers and emphasizing aligned embeddings although the required compute would be significantly more.

Conclusion

Deduplication of videos at scale requires a blend of techniques, including video segmentation, CLIP embeddings, and temporal alignment. Massive video assets can be efficiently managed by utilizing CUDA acceleration, clustering algorithms, and advanced embedding models. This architecture optimizes storage and ensures data quality, enabling downstream applications like content recommendation and analytics to be free from bias.