K-means cluster diversified searching method on manifold surface and based on geodesic distance

A technology of k-means clustering and geodesic distance, which is applied in the field of diversified image retrieval systems, can solve the problems of clustering without suitable indicators and inappropriateness, and achieve the effect of diversity

Inactive Publication Date: 2012-10-24
HEFEI UNIV OF TECH
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Problems solved by technology

At the same time, in traditional clustering methods, Euclidean distance is usually used to calculate the distance from a point to the center, which is not suitable for structures with potential manifolds, and there is no s

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  • K-means cluster diversified searching method on manifold surface and based on geodesic distance
  • K-means cluster diversified searching method on manifold surface and based on geodesic distance
  • K-means cluster diversified searching method on manifold surface and based on geodesic distance

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[0040] Such as figure 1 As shown, the K-means clustering diversified retrieval method based on geodesic distance on the manifold surface specifically includes the following steps:

[0041] (1) First, perform feature extraction on the training data set, and use SVM classifiers with different parameters to train and learn the extracted features;

[0042] (2) Filter the parameters of the SVM classifier with the authentication set data, and select the optimal parameters as the best SVM classifier;

[0043] (3) Perform feature extraction on the input test image and use it as the input data of the best SVM classifier, so as to obtain the order of the correlation between the image in the database and the input image;

[0044] (4) Use the DB indicator to filter the buffer pool size parameters;

[0045] When selecting the size of the buffer pool, two evaluation indicators are used: the retrieval accuracy Pn of the first n images, and the number of sub-concepts covered by the first n images CRn;...

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Abstract

The invention discloses a K-means cluster diversified searching method on the manifold surface and based on geodesic distance. The K-means cluster diversified searching method specifically includes the following steps: extracting features, training and generating a plurality of support vector machines (SVM) with different parameters and choosing an optimal SVM; extracting input image features, and executing searching through the optimal SVM to generate result sequencing; choosing values of a buffering pool and k values adjacent to k by means of a data base (DB) index; performing generic space division on a training set, and adopting an improved K-means cluster method; and obtaining images closest to cluster centers by adopting the geodesic distance, and finally leading out final sequencing. By means of the K-means cluster diversified searching method, the content-based image searching technology is used, recognition and searching on images are automatically achieved, optimal parameters are well chosen, diversification of searching results is achieved under the premise that relevance is ensured, similar or approximately similar searching results are hidden for users, results with representativeness are extracted, and diversified information is supplied to users within time as far as short.

Description

Technical field [0001] The invention relates to an image diversified retrieval system, in particular to a K-means clustering diversified retrieval method based on geodesic distance on a manifold surface. Background technique [0002] The object of image diversification retrieval research is: how to make novel, unique, non-repetitive and redundant images rank higher in the retrieval results in image retrieval. The application fields of the diversified image retrieval system mainly include: image electronic equipment application, Internet image search, medical image retrieval, commercial information retrieval, remote sensing information investigation and so on. In recent years, with the rapid development and popularization of digital equipment such as scanners, digital cameras, and digital video cameras, as well as the improvement of multimedia technology and the rapid popularization of the Internet, image data has grown geometrically, and large capacity has emerged. Faced with th...

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Application Information

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IPC IPC(8): G06F17/30
Inventor 赵仲秋马林海吴信东高隽
Owner HEFEI UNIV OF TECH
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