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A learning framework for shape retrieval based on multilayer perceptrons
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DOI：10.1016/j.patrec.2018.09.005.png)
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With the rapid development of 3D technology, the demand to use and retrieve 3D models has become increasingly urgent. In this paper, we present a framework that consists of a sketch-based local binary pattern (SBLBP) feature extraction method, a learning algorithm for the best view of a shape based on multilayer perceptrons (MLPs) and a learning method for shape retrieval based on two Siamese MLP networks. The model is first projected into many multiview images. A transfer learning scheme based on graphic traversal to identify Harris key points is proposed to build relations between view images and sketches. In addition, an MLP classifier is used for classification to obtain the best views of each model. Moreover, we propose a new learning method for shape retrieval that simultaneously uses two Siamese MLP networks to learn SBLBP features. Furthermore, we build a joint Bayesian method to fuse the outputs of the views and sketches. Based on training with many samples, the MLP parameters are effectively fit to perform shape retrieval. Finally, an experiment is conducted to verify the feasibility of the approach, and the results show that the proposed framework is superior to other approaches. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Sketch-based local binary patterns
Siamese MLP
Transfer learning
Joint Bayesian
Shape retrieval
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