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Cross-media Hash index method based on coupling differential dictionary

A hash index, coupled dictionary technology, applied in the field of cross-media retrieval, can solve problems such as inability to meet users' online search needs, high time complexity, etc.

Inactive Publication Date: 2015-01-28
ZHEJIANG UNIV
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  • Abstract
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Problems solved by technology

2) The amount of multimedia data is very large, and the traditional violent linear search strategy will generate extremely high time complexity when processing large-scale data, which cannot meet the online search needs of users

Method used

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  • Cross-media Hash index method based on coupling differential dictionary
  • Cross-media Hash index method based on coupling differential dictionary
  • Cross-media Hash index method based on coupling differential dictionary

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Experimental program
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Embodiment

[0073] In order to verify the effect of the present invention, about 2,900 webpages were grabbed from "Wikipedia feature articles" (Wikipedia feature articles), and each webpage contained an image and the related description text of the image. Use this as a data set to carry out the retrieval experiment based on the cross-media hash index in the present invention (ie image retrieval text or text retrieval image). For image modality data, the present invention extracts SIFT features in the image, and then uses K-means clustering to cluster the SIFT features to obtain 1000 centroids of SIFT features. Finally, each image is quantified into a 1000-dimensional "Bag of visual words" (Bag of visual words); for text modal data, the most representative 5000 words are selected according to the TF value of each word, so each The text modal data is expressed as a 5000-dimensional "textual word bag" (Bag of textual words).

[0074] In order to further verify the effect of the present inve...

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Abstract

The invention discloses a cross-media Hash index method based on a coupling differential dictionary. The cross-media Hash index method based on the coupling differential dictionary comprises the following steps that (1) modeling is conducted on the correlation of a plurality of modal data based on a graph structure, the similarity inside a modal is determined through the Euclidean distance between the data low-level features, the correlation between modals is determined by using the known correlation of different modal data, and classification label information of the data is used for improving the differentiation of the data on the graph structure; (2) the differential coupling dictionary is studied on the correlation of the data on the graph structure obtained in the step (1); (3) sparse coding is conducted on the different modal data by using the studied coupling dictionary in the step (2) and mapped inside unified dictionary space; (4) a Hash mapping function from the dictionary space to binary hamming space is studied. The cross-media Hash index method based on the coupling differential dictionary can realize the efficient cross-media searching of mass data based on content, and a user can submit a searching example of one modal to search a media object of another modal.

Description

technical field [0001] The invention relates to cross-media retrieval, in particular to a cross-media efficient indexing method based on massive data. Background technique [0002] With the rapid development of Internet technology and the popularity of social networks, the amount of multimedia data on the Internet is growing at an alarming rate. Multimedia data has the following characteristics: 1) Due to the complex semantics of multimedia data, it is difficult to measure directly. In order to realize the measurement of multimedia data, the features of the media data are generally extracted first, and then the similarity between the features is taken as the similarity between the media data. Generally, these extracted features are often high-dimensional, so the retrieval problem of multimedia data is transformed into a retrieval problem of high-dimensional data. 2) The amount of multimedia data is very large, and the traditional violent linear search strategy will generat...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/9014
Inventor 汤斯亮邵健余宙吴飞庄越挺
Owner ZHEJIANG UNIV
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