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Spatially Enhanced Bags of Words for 3D Shape Retrieval

Published

Author(s)

Xiaolan Li, Afzal A. Godil, Asim Wagan

Abstract

This paper presents a new method for 3D shape retrieval based on the bags-of-words model along with a weak spatial constraint. First, a two-pass sampling procedure is performed to extract the local shape descriptors, based on spin images, which are used to construct a shape dictionary. Second, the model is partitioned into different regions based on the positions of the words. Then each region is denoted as a histogram of words (also known as bag-of-words) as found in it along with its position. After that, the 3D model is represented as the collection of histograms, denoted as bags-of-words, along with their relative positions, which is an extension of an orderless bag-of-words 3D shape repre-sentation. We call it as Spatial Enhanced Bags-of-Words (SEBW). The spatial constraint shows improved performance on 3D shape retrieval tasks.
Proceedings Title
Proceeding of the 4th International Symposium on Visual Computing
Volume
5358
Conference Dates
December 1-3, 2008
Conference Location
Las Vegas, NV
Conference Title
4th International Symposium on Visual Computing

Keywords

spin image, bag of words, 3D shape retrieval

Citation

Li, X. , Godil, A. and Wagan, A. (2008), Spatially Enhanced Bags of Words for 3D Shape Retrieval, Proceeding of the 4th International Symposium on Visual Computing, Las Vegas, NV, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=890073 (Accessed April 23, 2024)
Created December 3, 2008, Updated February 19, 2017