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Title: Efficient Retrieval and Categorization for 3D Models based on Bag-of-Words Approach
Authors: WANG YAN
Keywords: 3D model, retrieval, categorization, bag-of-words model
Issue Date: 14-Aug-2013
Source: WANG YAN (2013-08-14). Efficient Retrieval and Categorization for 3D Models based on Bag-of-Words Approach. ScholarBank@NUS Repository.
Abstract: Efficient retrieval and categorization of 3D models are in urgent need due to the rapid proliferation of 3-Dimensional (3D) digital models. Recently, bag-of-words approach based on the visual similarity for 3D model retrieval has received a lot of attention for its superior performance and scalability to various input formats. It represents 3D model as histogram of visual words according to a codebook generated from local features extracted from 2D depth images. However, existing salient feature extraction methods not only are time-consuming, but also require large computation and storage capacity. Besides, very little research work has addressed 3D model categorization problem compared to large amount of work for the 3D model retrieval tasks. The categorization of 3D models is of great importance because when the database is huge, it is impossible to compare the query example with all target models, so there is a need for a mechanism to classify the query models into categories. This research aims at achieving two main objectives. The first objective is to develop more discriminative but computationally less expensive feature extraction methods. The second objective is to develop a 3D model categorization system which is very little addressed in the past. Both of the two objectives are achieved based on the bag-of-words framework. Firstly, a modified dense sampling and multi-scale dense (MSD) sampling strategy of local salient features are proposed to extract features from depth images of 3D models. Dense sampling is to extract features on uniformly distributed grids and MSD sampling is to extract features at multiple scales on the same grids as dense sampling. The proposed sampling strategies extract local features over the full range of the depth images rendered from the 3D model and therefore more suitable for the 3D model description. With a flat window to substitute circular Gaussian window, the feature extraction speed for the proposed sampling strategies are in an order of magnitude faster than the original Scale Invariant Feature Transform (SIFT) detection. In combination with bag-of-words models, the proposed sampling strategies have shown superior performance over the original salient SIFT sampling. Secondly, two region feature descriptors Region Speeded-Up Robust Features (RSURF) and Histogram of Oriented Gradients (HOG) features are proposed for 3D model description. The proposed RSURF and HOG features extract features on uniform grids over a local region. As they extract features with a pre-assumed scale and location, the proposed region-based feature detections are much faster and of lower dimension than the salient point detection. The region size, number of orientation bins and coarse spatial binning will influence the descriptiveness and distinctness of the region-based feature descriptor together. The proposed region feature descriptors are used as inputs for bag-of-words model and show a much better accuracy than salient feature description for the 3D model retrieval tasks. Thirdly, a 3D model categorization scheme based on the bag-of-words representation is proposed using kernelized multi-class SVM for classification. The chi-square kernel and histogram intersection kernel approximated by linear homogeneous map are adopted as they are inherently suitable for the histogram-based shape representation. The linearly approximated kernel SVM not only show significant improvement than the original SVM, but are also very efficient to compute. Example of the proposed3D model categorization system will be given for classification of query examples on public shape benchmark.
Appears in Collections:Ph.D Theses (Open)

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