Image retrieval method based on CNN (Convolutional Neural Network) feature vocabulary tree

A technology of image retrieval and vocabulary tree, which is applied in the field of image processing, can solve problems such as limited accuracy, low image retrieval accuracy, and inability to fully describe the high-level semantics of images, achieving the effect of improving accuracy, stability and effectiveness

Active Publication Date: 2017-12-01
XIDIAN UNIV
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Problems solved by technology

This method uses the feedback information of user search results to modify the semantic mapping, which improves the accuracy of image retrieval. However, due to the lack of effective user feedback information in practical applications, the actual ac

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  • Image retrieval method based on CNN (Convolutional Neural Network) feature vocabulary tree
  • Image retrieval method based on CNN (Convolutional Neural Network) feature vocabulary tree
  • Image retrieval method based on CNN (Convolutional Neural Network) feature vocabulary tree

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Embodiment Construction

[0042] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0043] The images used in this embodiment are all from the mammogram database DDSM. The number of images in the image library used is N=2000, including 1000 breast normal tissue images and 1000 breast mass images, and the number of images to be retrieved is N=400, including 200 breast normal tissue images and 200 breast mass images image.

[0044] Refer to attached figure 1 , an image retrieval method based on CNN feature vocabulary tree, comprising the following steps:

[0045] Step 1) extract the CNN features of each image in the image bank:

[0046] Step 1a) Rotate each image in the image library at multiple angles around its center, and perform central axisymmetric transformation at the same time, and intercept the subgraphs of the four corners of each image and various sides concentric with each image The longest subgraph obt...

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Abstract

The invention discloses an image retrieval method based on a CNN (Convolutional Neural Network) feature vocabulary tree, and aims to solve the technical problem of low accuracy in an existing vocabulary tree method. The method comprises the following implementation steps that: firstly, generating the derivative image of each image in an image library, and extracting the CNN feature of each image in the image library; according to the extracted CNN feature, constructing the CNN feature vocabulary tree; then, generating the derivative image of each image to be retrieved, and extracting the CNN feature of each image to be retrieved; comparing the path of the CNN feature of each image to be retrieved with the path of the CNN feature of the relevant image of the image to be retrieved in the CNN feature vocabulary tree; calculating a distance between the image to be retrieved and the relevant image, and combining the distance between the image to be retrieved and the relevant image with initial similarity; and finally, according to the comprehensive similarity of each image to be retrieved and the relevant image, outputting the retrieval result of each image to be retrieved. The method is high in image retrieval accuracy and can be used for a medical image computer-assisted diagnostic system and a search-by-image system.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image retrieval method, in particular to an image retrieval method based on a CNN feature vocabulary tree. It can be used in medical image computer aided diagnosis system and image search system. Background technique [0002] Image retrieval is the process of comparing the similarity between the description content input by the user and the images in the image library according to a certain similarity comparison mechanism, and returning similar images. With the development of science and technology, the number of images is increasing explosively, and the difficulty of image retrieval is also increasing. [0003] According to the different input description content, image retrieval can be divided into text-based image retrieval and content-based image retrieval. Text-based image retrieval requires a lot of manual labeling, which consumes a lot of manpower and material ...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06K9/62
CPCG06F16/5838G06F18/22
Inventor 王颖李洁陈佳丽焦志成范淼薛学通王斌路文何立火
Owner XIDIAN UNIV
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