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Multi-perspective target retrieval method

A multi-view and target technology, applied in the field of multi-view target retrieval, can solve the problem that the retrieval accuracy needs to be improved, the relevance and importance of the same object are not considered, and the effect is good and the accuracy is improved.

Inactive Publication Date: 2015-01-21
TIANJIN UNIV
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AI Technical Summary

Problems solved by technology

[0005] When current multi-view target retrieval algorithms calculate the similarity between two objects, most of them only calculate the Euclidean distance between the corresponding views of the two objects, without considering the relevance and importance of the views of the same object. The accuracy rate needs to be improved

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Embodiment Construction

[0018] In order to make the purpose, technical solution and advantages of the present invention clearer, the implementation manners of the present invention will be further described in detail below.

[0019] The embodiment of the present invention provides a method for multi-view object retrieval, see figure 1 , the method includes:

[0020] 101: Obtain the retrieval target input by the user and the view set of objects in the database;

[0021] Wherein, the retrieval target input by the user and the view set of objects in the database represent a corresponding set of two-dimensional views of these three-dimensional objects. These two-dimensional views can be obtained by shooting real three-dimensional objects with real cameras, or by shooting virtual three-dimensional objects with virtual cameras of 3D program software (such as 3D MAX).

[0022] 102: Using an image feature extraction algorithm to perform feature extraction on the retrieval target and the view set of objects...

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Abstract

The invention discloses a multi-perspective target retrieval method. The multi-perspective target retrieval method includes steps of acquiring a retrieval target input by a user and view sets of objects in database; utilizing the image characteristics extraction algorithm to extract characteristics of the retrieval target and the objects in the database; clustering the view sets by a clustering method after characteristics extraction and extracting representative views of each type; determining corresponding initial weight of each representative view according to scale of the belonging type and updating the weights by means of relation between the representative views and generating the final weights; establishing a weighted bipartite graph by means of the representative views of the two view sets and their weights; seeking the optimal matching of the weighted bipartite graph by the bipartite graph matching algorithm and acquiring similarity between the retrieval target and each object in the database, sequencing and outputting the sequencing result as retrieval results. By the multi-perspective target retrieval method, accuracy of multi-perspective target retrieval is improved.

Description

technical field [0001] The invention relates to the field of image retrieval, in particular to a method for multi-view object retrieval. Background technique [0002] The existence of objects in real life is spatial, and the human eye's perception of objects is also three-dimensional. The traditional camera technology only obtains the two-dimensional plan view of the object, while the RGB-D (three primary colors plus distance) camera, such as the Kinect three-dimensional somatosensory camera, can not only obtain two-dimensional information, but also obtain corresponding depth information, which makes up for the The flaws of traditional video cameras. Compared with images, 3D models can express richer perceptual details and are closer to the three-dimensional real feeling of human eyes, so they are more suitable for human cognitive experience. [0003] The acquisition of 3D models is a major issue that must be considered. If a set of 3D models needs to be established every...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06K9/46
CPCG06F16/583G06F18/23213
Inventor 刘安安苏育挺曹群
Owner TIANJIN UNIV
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