Cross-media feature learning retrieval method based on semi-supervision

Abstract

The invention provides a cross-media feature learning retrieval method based on semi-supervision. The method comprises the following steps that: S1: establishing a multimedia database; S2: solving theprojection matrixes of different media types; and S3: carrying out cross-media retrieval; and S3: carrying out cross-media retrieval. The S2 comprises the following steps that: 2.1: defining a targetfunction; 2.2: optimizing the target function; and 2.3: projecting the original feature of the multimedia data to a public space. The S3 comprises the following steps that: 3.1: extracting the feature of the media data submitted by a user: according to the media type of the data submitted by the user, using a pre-trained model to extract the feature of the data; 3.2: projecting the feature vectorof the media data into a common space; 3.3: calculating a similarity between the projected feature vector and other vectors in the common space; and 3.4: returning the first k pieces of media data with the highest similarity. By use of the method, calculation complexity is lowered, noise robustness is realized, and retrieval accuracy is improved.

Description

technical field [0001] The invention relates to a semi-supervised-based cross-media feature learning retrieval method, which belongs to the field of retrieval. Background technique [0002] With the development of modern computer science and technology, multimedia data such as images, texts, and videos on the Internet are increasing rapidly. Content-based multimedia retrieval has become more and more important, and a lot of research has been done on it. Traditional content-based retrieval methods usually focus on single mode retrieval, such as image retrieval, text retrieval. In this case, query and retrieval results are of the same media type. However, single-mode retrieval cannot fully exploit diverse media data. To solve this problem, cross-media retrieval has been proposed and becomes more and more important. It is designed to query one type of data as a query to retrieve related data objects of another type. For example, users can use pictures of tigers to retrieve...

AI Technical Summary

Problems solved by technology

However, the dimensionality of the original features is usually large, which affects the computational complexity of the algorithm

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