Multi-kernel hash learning-based large-scale medical image retrieval method

A medical image, large-scale technology, applied in the field of image processing, can solve the problems of high image dimension, low retrieval speed, large image scale, etc., and achieve the effect of reducing storage space, workload, and storage capacity.

Active Publication Date: 2017-03-08
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0005] The invention proposes a large-scale medical image retrieval method based on multi-core hash learning based on the problems of high image dimension, complex calculation, and the problem of "dimension disaster"; large image scale, low retrieval speed, and large storage capacity.

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  • Multi-kernel hash learning-based large-scale medical image retrieval method
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  • Multi-kernel hash learning-based large-scale medical image retrieval method

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Embodiment Construction

[0027] In the present invention, a suitable kernel function is selected to combine, and the data is mapped into a high-dimensional data space to solve the problem of linear inseparability.

[0028] Different kernel functions have their own advantages and disadvantages, and the characteristics of different kernel functions are also different, and the performance of the combined kernel functions composed of them will also be different.

[0029] Kernel functions are mainly divided into global kernel functions and local kernel functions. Global kernel functions (such as linear kernel functions) have global characteristics, allowing data points that are far apart to have an influence on the value of the kernel function, while local kernel functions (such as Gaussian kernel functions) have locality, allowing only close distances. The data points have an effect on the value of the kernel function. Combining the respective advantages of different kernel functions, the present inventi...

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Abstract

The invention discloses a multi-kernel hash learning-based large-scale medical image retrieval method. The method concretely comprises the steps of: constructing a kernel matrix through fusion of a plurality of different kernel functions; completely converting an image into hash codes by use of the learned hash function, and carrying out compression on the hash codes; solving distances of medical images through hamming distance measurement, sorting according to an ascending order, selecting m images with minimum distances to be returned to a user; optimizing the sorting of the retrieved images again by the user by use of a relevance feedback algorithm until the sorting satisfies the requirement of the user. The method is high in calculation efficiency, rapid in retrieval speed, small in memory space, high in retrieval precision, clear in steps and strong in pertinence, contributes to medical diagnosis of a doctor, reduces the workload of the doctor and improves the working efficiency.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to the realization of large-scale medical image retrieval by hash learning of multi-kernel function fusion. Background technique [0002] Image retrieval technology refers to retrieving matching images or similar images from an image database according to an input image. The existing technologies mainly include three aspects: text-based image retrieval technology, content-based image retrieval technology, and retrieval technology combining text and images. The main limitations of text-based techniques are the subjective tendencies and semantic limitations of text annotation. [0003] Content-based image retrieval technology is the mainstream technology in current research, but there are some technical difficulties: (1) there is no universally applicable method that can be applied to various fields of image retrieval; (2) the images are getting bigger and bigger, and the dimensions a...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/51G06F16/583
Inventor 曾宪华袁知洪马雪
Owner CHONGQING UNIV OF POSTS & TELECOMM
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