Hybrid associative retrieval of three-dimensional models.
In this paper, we propose a novel 3-D model retrieval framework, which is referred to as hybrid 3-D model associative retrieval. Unlike the conventional 3-D model similarity retrieval approach, the query model and the models obtained by 3-D model hybrid associative retrieval have the following properties: They belong to different model classes and have different shape characteristics in general but are semantically related and preassembled in a certain associative group. For instance, given a furniture associative group { desk, chair, bed}, we may probably like to use a desk as a query model to search for a list of matching models, which belong to the chair or bed class. We consider the following possibilities: 1) there can be more than two classes in an association group and 2) different association groups might have different numbers of classes. The hybrid associative retrieval is performed in two stages: 1) to establish the relationship between different 3-D model categories with semantic associations, we propose three approaches based on neural network learning and 2) to address the aforementioned two conditions, we use a cyclic-shift scheme to partition different associative groups into two-class pairwise associative groups and then adopt two different strategies to combine the final retrieval results. Experiments by using different data sets demonstrate the effectiveness and efficiency of our proposed framework on the new hybrid associative retrieval task.
Zhang
Shaohong
S
Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong. shazhang@student.cityu.edu.hk
Wong
Hau-San
HS
Yu
Zhiwen
Z
Ip
Horace H S
HH
D000465:Algorithms; D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D003628:Database Management Systems; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D011873:Radiology Information Systems