Three-dimensional object retrieval method based on multi-view classification

Abstract

The invention discloses a three-dimensional object retrieval method based on multi-view classification. The method comprises the steps of obtaining an original image; processing the original image toobtain a multi-view image; processing the multi-view image to obtain a feature descriptor; processing the feature descriptor to obtain a vocabulary tree; and retrieving a to-be-retrieved image in thevocabulary tree to obtain the serial number of the original image. According to the method, a multi-view classification algorithm is adopted to obtain an omnibearing multi-view feature image of the three-dimensional object in a two-dimensional plane, the three-dimensional object can be directly retrieved, the method is simple, convenient and easy to implement, and the problem that the retrieval performance is reduced due to view angle changes of the object shot by a user in three-dimensional image retrieval is solved.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a three-dimensional object retrieval method based on multi-view classification. Background technique [0002] As an important means for human beings to understand the world, computer vision technology has been widely concerned by researchers. Due to the rapid development and popularization of camera technology, people are exposed to a variety of explosive image information through multimedia devices such as mobile phones. Nowadays, two-dimensional images are widely used in transportation, medical, military and other fields. Image retrieval is an information processing technology for image information processing. Researchers have done a lot of work and achieved rich results. In real life, what people face is a three-dimensional object, and the real three-dimensional scene is what people come into contact with every day. With the development of three-dimensiona...

AI Technical Summary

Problems solved by technology

However, the perspective characteristics of the 3D object image obtained by the first method are not comprehensive; the establishment and description of the 3D model by the second method are relatively difficult; the third method cannot directly retrieve the 3D object, and the 3D information must be converted to convert into two-dimensional information for description, classification and identification

[0004] To sum up, the existing technology cannot realize the image description of three-dimensional objects in all directions and multiple perspectives when searching for three-dimensional objects, and cannot directly retrieve three-dimensional objects, and requires complex three-dimensional modeling

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