Deep convolutional neural network end-to-end based image retrieval method by layered deep searching

A convolutional neural network and neural network technology, applied in the field of image retrieval, can solve problems such as low level of automation and intelligence, difficulty in obtaining accurate search results, slow retrieval speed and image retrieval requirements

Active Publication Date: 2016-12-14
汤一平
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  • Application Information

AI Technical Summary

Problems solved by technology

[0013] In order to overcome the low level of automation and intelligence in the existing image search technology, the lack of deep learning, the difficulty in obtaining accurate search results, the large consumption of storage space in retrieval technology, and the slow retrieval speed, it is difficult to meet the image retrieval requirements in the era of big data, etc. Insufficient,

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  • Deep convolutional neural network end-to-end based image retrieval method by layered deep searching
  • Deep convolutional neural network end-to-end based image retrieval method by layered deep searching
  • Deep convolutional neural network end-to-end based image retrieval method by layered deep searching

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Embodiment 1

[0143] refer to Figure 1-11 , the technical solution adopted by the present invention to solve its technical problems is:

[0144] An end-to-end approach to image retrieval via hierarchical deep search based on deep convolutional neural networks includes a convolutional neural network for deep learning and trained recognition, a fast visual segmentation algorithm for searching image objects, a method for coarse search A method for fast image alignment using hashing and Hamming distance and an exact alignment method for top-k ranked images based on images from candidate pool P;

[0145] (1) About designing a fast visual segmentation algorithm for searching image objects;

[0146] Since in most applications, the search image object is only a part of the whole image, especially in road monitoring and bayonet image comparison search, it is necessary to design a fast visual segmentation algorithm for search image objects to improve search efficiency ;

[0147] First, a fast vis...

Embodiment 2

[0254] The technology of image search in the present invention has universal applicability, and is suitable for network image search engine, video investigation and bayonet analysis and judgment, as long as the image data participating in the training is operated in the system developed by the present invention to learn, and obtain such objects (such as a specific person, a specific vehicle, etc.) after the characteristics of the search task can be realized.

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Abstract

The invention discloses a deep convolutional neural network end-to-end based image retrieval method by layered deep searching. The method is characterized by mainly including a convolutional neural network used for deep learning, training and recognition, a rapid visual segmentation algorithm for searching image objects, a quick comparison method used for rough searching with a Hash method and Hamming distance fast images, and a precise comparison method for first k ranked images based on images from a candidate pool P. By the method, automation and intelligence level in searching images with images can be effectively heightened, search results can be acquired accurately, and demand of image retrieval in the big data era is satisfied with less storage space and high retrieval speed.

Description

technical field [0001] The present invention relates to the application of database management, computer vision, image processing, pattern recognition, information retrieval, deep neural network and deep learning technology in the field of image retrieval, especially to an end-to-end layered deep search based on deep convolutional neural network image retrieval method. Background technique [0002] Image retrieval, searching for images by image, is a technology for retrieving similar images by inputting images, and provides users with a search technology for retrieving related graphic and image data. The technology involves many disciplines such as database management, computer vision, image processing, pattern recognition, information retrieval and cognitive psychology. Its related technologies mainly include two key technologies: feature representation and similarity measurement. It is widely used in various fields such as big data graphic image retrieval, video detectio...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06K9/46G06K9/62G06N3/08
CPCG06F16/5838G06F16/5862G06N3/084G06V10/44G06F18/22
Inventor 汤一平
Owner 汤一平
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