The semi-explicit shape model for multi-object detection and classification

Simon Polak*, Amnon Shashua

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


We propose a model for classification and detection of object classes where the number of classes may be large and where multiple instances of object classes may be present in an image. The algorithm combines a bottom-up, low-level, procedure of a bag-of-words naive Bayes phase for winnowing out unlikely object classes with a high-level procedure for detection and classification. The high-level process is a hybrid of a voting method where votes are filtered using beliefs computed by a class-specific graphical model. In that sense, shape is both explicit (determining the voting pattern) and implicit (each object part votes independently) - hence the term "semi-explicit shape model".

Original languageAmerican English
Title of host publicationComputer Vision, ECCV 2010 - 11th European Conference on Computer Vision, Proceedings
PublisherSpringer Verlag
Number of pages14
EditionPART 2
ISBN (Print)3642155510, 9783642155512
StatePublished - 2010
Event11th European Conference on Computer Vision, ECCV 2010 - Heraklion, Crete, Greece
Duration: 10 Sep 201011 Sep 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6312 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th European Conference on Computer Vision, ECCV 2010
CityHeraklion, Crete

Bibliographical note

Funding Information:
This work was partially funded by ISF grant 519/09.


Dive into the research topics of 'The semi-explicit shape model for multi-object detection and classification'. Together they form a unique fingerprint.

Cite this