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
Conference Dates: December 1-3, 2008
Conference Location: Las Vegas, NV
Conference Title: 4th International Symposium on Visual Computing
Pub Type: Conferences
spin image, bag of words, 3D shape retrieval