Cross-media retrieval method based on subspace learning and semi-supervised regularization

Abstract

The invention provides a cross-media retrieval method based on subspace learning and semi-supervised regularization, which is characterized by comprising the following steps: Step 1, establishing a multimedia database, collecting multimedia original data, extracting features of multimedia data, and storing feature vectors of the multimedia data and the original data; Step 2, acquiring a projectionmatrix of different media types, defining an optimal target function, solving the optimal target function by utilizing an iterative method, and projecting the feature vectors of the multimedia data to a public space; Step 3, carrying out cross-media retrieval, extracting features of media data submitted by a user, projecting feature vectors of the media data into the public space, calculating a similarity between the projected vectors and other vectors in the public space, and returning the media data corresponding to first k feature vectors with the largest similarity with other vectors in the public space. The cross-media retrieval method provided by the invention generates a more accurate retrieval result.

Description

technical field [0001] The invention relates to a cross-media retrieval method based on subspace learning and semi-supervised regularization, belonging to the field of data retrieval. Background technique [0002] With the rapid development of multimedia technology and network technology, unstructured heterogeneous multimedia content such as text, image, audio, video, and 3D floods into the Internet rapidly, making cross-media retrieval particularly important. Cross-media retrieval refers to the mutual retrieval between different media data, which makes the retrieval rich and colorful, and better meets the needs of users who want to submit any kind of media data to retrieve various types with the same semantics (same type or different types) of media data needs. [0003] At present, more and more domestic and foreign scholars are devoted to the study of cross-media retrieval, and the proposed methods can be roughly classified into the following categories: deep learning, pr...

AI Technical Summary

Problems solved by technology

The disadvantages are: most of the existing cross-media retrieval methods are limited to the retrieval between two media, and there is a problem that the calculation of the weight matrix in the process of constructing the neighbor graph is too cumbersome

[0004] These traditional retrieval methods only focus on the retrieval between the same media or two media data, which ignores the semantic consistency and complementary relationship between multiple media data.

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