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Lior Litwak

dblp:44/10771 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2011
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data mining · 100%
Computer graphics and multimedia
1 paper
Image and video processing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining › clustering › interactive clustering
active clustering
0.112011
Active clustering of document fragments using information derived from both images and catalogs · ICCV 2011
Data mining
clustering
0.112011
Active clustering of document fragments using information derived from both images and catalogs · ICCV 2011
Image and video processing
document image analysis
0.112011
Active clustering of document fragments using information derived from both images and catalogs · ICCV 2011

Methods — techniques the papers use, named apart from their topics

graphical model · 0.2active clustering · 0.2
YearPublicationVenuePosition
2011 Active clustering of document fragments using information derived from both images and catalogs
abstract
Many significant historical corpora contain leaves that are mixed up and no longer bound in their original state as multi-page documents. The reconstruction of old manuscripts from a mix of disjoint leaves can therefore be of paramount importance to historians and literary scholars. Previously, it was shown that visual similarity provides meaningful pair-wise similarities between handwritten leaves. Here, we go a step further and suggest a semiautomatic clustering tool that helps reconstruct the original documents. The proposed solution is based on a graphical model that makes inferences based on catalog information provided for each leaf as well as on the pairwise similarities of handwriting. Several novel active clustering techniques are explored, and the solution is applied to a significant part of the Cairo Genizah, where the problem of joining leaves remains unsolved even after a century of extensive study by hundreds of scholars.
Lior Wolf, Lior Litwak, Nachum Dershowitz, Roni Shweka, Yaacov Choueka
ICCV2