Bag of Bags: Adaptive Visual Vocabularies for Genizah Join Image Retrieval

TL;DR

This paper introduces Bag of Bags (BoB), a novel image representation for manuscript join retrieval that outperforms traditional Bag of Words methods by using fragment-specific vocabularies and set-to-set distance comparisons.

Contribution

The paper proposes a new adaptive visual vocabulary method, BoB, with a mass-weighted variant and a two-stage retrieval pipeline, improving accuracy and efficiency in manuscript fragment retrieval.

Findings

BoB (Chamfer) achieves 78% Hit@1, 84% MRR, outperforming baseline by 6.1%.

Mass-weighted BoB-OT provides formal bounds on approximation deviation.

Two-stage pipeline balances retrieval accuracy and computational cost.

Abstract

A join is a set of manuscript fragments identified as originally emanating from the same manuscript. We study manuscript join retrieval: Given a query image of a fragment, retrieve other fragments originating from the same physical manuscript. We propose Bag of Bags (BoB), an image-level representation that replaces the global-level visual codebook of classical Bag of Words (BoW) with a fragment-specific vocabulary of local visual words. Our pipeline trains a sparse convolutional autoencoder on binarized fragment patches, encodes connected components from each page, clusters the resulting embeddings with per-image k-means, and compares images using set-to-set distances between their local vocabularies. Evaluated on fragments from the Cairo Genizah, the best BoB variant (viz. Chamfer) achieves Hit@1 of 0.78 and MRR of 0.84, compared to 0.74 and 0.80, respectively, for the strongest BoW baseline (BoW-RawPatches-$\chi^2$), a 6.1% relative improvement in top-1 accuracy. We furthermore study a mass-weighted BoB-OT variant that incorporates cluster population into prototype matching and present a formal approximation guarantee bounding its deviation from full component-level optimal transport. A two-stage pipeline using a BoW shortlist followed by BoB-OT reranking provides a practical compromise between retrieval strength and computational cost, supporting applicability to larger manuscript collections. The code and dataset are available at https://github.com/TAU-CH/midrash_bob.