Multi-classifier integration method based on maximum expected parameter estimation

A parameter estimation and multi-classifier technology, applied in the field of image retrieval based on correlation feedback, which can solve the problems of weak classifier stability and large classification error.

Inactive Publication Date: 2011-04-20
LIAONING NORMAL UNIVERSITY
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Problems solved by technology

[0004] In view of the problems existing in the above-mentioned prior art, the purpose of the present invention is to study and design a novel multi-classifier integ

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  • Multi-classifier integration method based on maximum expected parameter estimation
  • Multi-classifier integration method based on maximum expected parameter estimation

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Embodiment Construction

[0066] Specific embodiments of the present invention are shown in the accompanying drawings, with figure 1 The multi-classifier integration method based on the maximum expected parameter estimation is shown... The specific implementation process of the multi-classifier integration method based on the maximum expected parameter estimation in the present invention is shown in the accompanying drawing, including an extraction unit, a retrieval unit, a marking unit and a learning unit , the specific steps are as follows:

[0067] 1 extraction unit

[0068] In this link, we mainly extract the underlying visual features of each image in the image library, and then put the extracted features into the feature library. The underlying features mainly used in the present invention include color features, texture features and shape features.

[0069] 1) Color. The present invention uses the color histogram as the color feature; first, the color space is converted from RGB to HSV space, ...

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Abstract

The invention discloses a multi-classifier integration method based on maximum expected parameter estimation, mainly relating to a relative feedback-based new image search method for integrating a plurality of single classifiers by utilizing a maximum expected parameter estimation method. In the method, an extraction unit, a search unit, a marking unit and a studying unit are provided, the method comprises the following specific steps: firstly extracting low-level visual features of each image, such as color, texture, shape and the like; randomly selecting an image from an image library by a user, comparing the similarity of the image feature with that of the low-level feature of all images in the image library by using a Euclidean distance algorithm, ordering the similarity according to size and returning the first 10 images to the user; and judging whether the returned images and the previously selected images are in the same semantic group, if so, marking the returned images as images of positive instance and images of negative instance, putting the marked images into a support vector machine to train, and then feeding back the learned result to the user.

Description

technical field [0001] The multi-classifier integration method based on maximum expected parameter estimation according to the present invention belongs to the field of image retrieval based on correlation feedback in multimedia information, and mainly relates to a correlation-based method for integrating multiple single classifiers with a maximum expected parameter estimation method. Feedback Image Retrieval Methods. Background technique [0002] At present, with the rapid development of multimedia technology and the increasing popularity of Internet technology, the sources of digital images are becoming more and more extensive, and image information of several gigabytes will be generated in various fields every day. How to quickly and accurately find the information needed by users from the vast image information has become an urgent problem to be solved. Content-based image retrieval technology has emerged as the times require, and has become a research hotspot in the fie...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06K9/62
Inventor 王向阳陈景伟
Owner LIAONING NORMAL UNIVERSITY
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