iscc/iscc-book-covers

ISCC Codes for Amazon Book Covers

Amazon book covers enriched with full 256-bit ISCC (International Standard Content Code) identifiers for cover image identification, similarity search, and deduplication research. The image_url field links to cover images on Amazon CDN for preview.

What is ISCC?

The International Standard Content Code (ISO 24138:2024) is a content-derived identifier for digital media assets. Unlike traditional identifiers that are assigned arbitrarily, ISCC codes are generated algorithmically from the content itself, enabling:

• Content Identification: Identify content regardless of format or location
• Similarity Search: Find visually or semantically similar images
• Deduplication: Detect exact and near-duplicate content
• Provenance Tracking: Link derived works to their sources

ISCC Units

Each record contains five 256-bit ISCC-UNITs that capture different aspects of the content:

Unit	Field	Description
Meta-Code	iscc_meta	Similarity based on embedded metadata (filename, title)
Semantic-Code	iscc_semantic	AI-based visual semantic similarity (what the image depicts)
Content-Code	iscc_content	Perceptual image similarity (visual appearance)
Data-Code	iscc_data	Raw binary data similarity (file structure)
Instance-Code	iscc_instance	Cryptographic hash for exact matching (like SHA-256)

The iscc field contains the composite ISCC-CODE combining all units.

Dataset Structure

Data Fields

Field	Type	Description
image_url	string	Cover image URL on Amazon CDN
iscc	string	Full composite ISCC-CODE
iscc_meta	string	256-bit Meta-Code
iscc_semantic	string	256-bit Semantic-Code
iscc_content	string	256-bit Content-Code
iscc_data	string	256-bit Data-Code
iscc_instance	string	256-bit Instance-Code
source_row_id	string	Original row identifier (parent_asin) in source dataset
title	string	Book title
isbn	string	ISBN-13 (preferred) or ISBN-10
publisher	string	Publisher name and edition info

Data Splits

Split	Samples
train	3,079,720

Usage

Loading the Dataset

from datasets import load_dataset

ds = load_dataset("iscc/iscc-book-covers")

Viewing a Sample

sample = ds["train"][0]
print(f"ISCC: {sample['iscc']}")
print(f"Title: {sample['title']}")

Similarity Search Example

import iscc_core as ic

# Get two ISCC codes to compare
code1 = ds["train"][0]["iscc_content"]
code2 = ds["train"][1]["iscc_content"]

# Calculate hamming distance (0 = identical, 256 = maximally different)
distance = ic.iscc_distance(code1, code2)
print(f"Hamming distance: {distance}")

# Convert to similarity percentage
similarity = 1 - (distance / 256)
print(f"Similarity: {similarity:.1%}")

Finding Near-Duplicates

import iscc_core as ic

# Threshold for near-duplicates (adjust based on use case)
THRESHOLD = 32  # ~87.5% similarity

reference = ds["train"][0]["iscc_content"]

for i, row in enumerate(ds["train"]):
    distance = ic.iscc_distance(reference, row["iscc_content"])
    if distance <= THRESHOLD and i > 0:
        print(f"Near-duplicate found: row {i}, distance={distance}")

Semantic Similarity Search

import iscc_core as ic

# Find semantically similar images (same subject/concept)
reference = ds["train"][0]["iscc_semantic"]

similar = []
for i, row in enumerate(ds["train"]):
    distance = ic.iscc_distance(reference, row["iscc_semantic"])
    if distance <= 64:  # ~75% semantic similarity
        similar.append((i, distance))

# Sort by similarity
for idx, dist in sorted(similar, key=lambda x: x[1])[:5]:
    print(f"Row {idx} (distance={dist})")

Source Data

This dataset was derived from cogsci13/Amazon-Reviews-2023-Books-Meta.

Source Data

Book metadata from the Amazon Reviews 2023 dataset by McAuley Lab. Cover images hosted by Amazon CDN. This derivative dataset contains ISCC codes and references to the original images, not the images themselves. License: Research use only. Refer to original dataset terms.

Processing

ISCC codes were generated using:

• iscc-sdk - High-level ISCC generation
• iscc-sci - Semantic image codes (experimental)

All processing was performed on original resolution images downloaded from Amazon CDN.

Considerations

Intended Use

• Content identification and matching research
• Image similarity search algorithm development
• Deduplication system benchmarking
• Visual-semantic retrieval experiments
• ISCC-based indexing research

Limitations

• Semantic codes are generated using experimental AI models and may not capture all semantic nuances
• ISCC codes are sensitive to significant image modifications (heavy cropping, overlays, filters)
• Image URLs point to Amazon CDN and may become unavailable over time

Privacy

This dataset contains ISCC codes and image URL references derived from the source dataset. Refer to the original dataset documentation for privacy considerations.

Citation

If you use this dataset, please cite both this dataset and the original source:

This Dataset:

@dataset{iscc_book_covers,
  title = {{ISCC Codes for Amazon Book Covers}},
  author = {{ISCC Foundation}},
  year = {{2026}},
  publisher = {{Hugging Face}},
  url = {{https://huggingface.co/datasets/iscc/iscc-book-covers}}
}

Amazon Reviews 2023:

@article{hou2024bridging,
  title = {{Bridging Language and Items for Retrieval and Recommendation}},
  author = {{Hou, Yupeng and Li, Jiacheng and He, Zhankui and Yan, An and Chen, Xiusi and McAuley, Julian}},
  journal = {{arXiv preprint arXiv:2403.03952}},
  year = {{2024}}
}

ISCC Standard:

@misc{iso24138,
  title = {{ISO 24138:2024 Information and documentation -- International Standard Content Code (ISCC)}},
  author = {{International Organization for Standardization}},
  year = {{2024}},
  url = {{https://www.iso.org/standard/77899.html}}
}

Additional Resources

• ISCC Foundation - Standards organization
• ISCC Documentation - Technical documentation
• ISO 24138:2024 - Official standard
• iscc-sdk - Python SDK for ISCC generation
• Amazon Reviews 2023

Contact

• Dataset Issues: iscc-datasets GitHub
• ISCC Questions: ISCC Foundation