Learning-Based Similarity Metrics

Abstract

The invention discloses a similarity measurement method based on learning, which includes: given the views of the stereo model, screening representative views to construct a view-based hypergraph to represent the relationship between the stereo objects; using the stereo model data, Spatial structure circular descriptors are extracted from each stereotype, and the distance between each stereotype is used to generate a simple model-based graph to explore the correlation between stereotypes; an appropriate learning framework is selected, and an initial The learning weight of , the hypergraph generated based on the view and the graph generated based on the stereo model are used as the input of the learning framework, and the optimal combination weight of the two graphs is learned through the joint learning framework, so that through the hypergraph based on the view and the graph generated based on the stereo model graph to estimate the correlation between stereo objects. The invention makes the description of the three-dimensional model more comprehensive and more accurate and scientific in the aspect of similarity quantification by extracting the view feature information and the three-dimensional model space structure information.

Description

technical field

[0001] The invention relates to the fields of similarity measurement, three-dimensional model retrieval and the like, in particular to a learning-based similarity measurement method. Background technique

[0002] Due to the rapid development of graphics hardware, computer technology and network, three-dimensional objects have been widely used in various applications, such as: computer graphics, medical industry and virtual reality field. Large-scale databases of volumetric objects are increasing rapidly, leading to a gradual increase in the demand for efficient algorithms for volumetric object retrieval.

[0003] Recently, extensive research work has been devoted to stereoscopic object retrieval techniques [1]-[4] . Existing stereoscopic object retrieval methods can be simply divided into two paradigms, model-based and view-based methods.

[0004] In a model-based approach [5]-[7] In , volumetric objects are described as model-based features, e.g. low-lev...

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