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

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...

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, the present invention provides an end-to-end image retrieval method through layered depth search based on a deep convolutional neural network, which can effectively improve the automation and intelligence level of image search, obtain accurate search results, and use relatively Less storage space, faster retrieval speed and slower to meet the image retrieval needs in the era of big data

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