Large-scale image library retrieving method based on image Hash

Abstract

The invention discloses a large-scale image library retrieving method based on image Hash, which belongs to the technical field of image retrieval and relates to an image retrieving method based on contents. The large-scale image library retrieving method is characterized by comprising the following steps of: selecting a training image which is relevant to a query image from an image library to be retrieved; respectively extracting Gist characteristics of an image to be retrieved, the training image and the query image; clustering the training characteristics into a C category by a K average value clustering method; calculating a hypersphere classified function of each category of sample characteristics to define a Hash function as a Hash sequence for calculating the characteristics of the image to be retrieved and the characteristics of the query image; calculating the Hamming distance between the Hash sequence of the query image and the Hash sequence of the image to be retrieved; setting a threshold value d and returning similar images. The invention overcomes the defect of more Hash functions of an LSH method, solves the problem that a spectrum Hash method and a semantic Hash method can not be expanded to the core space and the selecting problem on samples when the Hash function is calculated by a KLSH method simultaneously.

Description

technical field The invention belongs to the technical field of image retrieval and relates to a content-based image retrieval method, in particular to a large-scale image database retrieval method based on image hashing. Background technique Content-based image retrieval has attracted the attention of researchers since its emergence in the 1990s, and many excellent technologies and methods have emerged. The research hotspots mainly focus on image feature representation, similarity measurement, and human feedback. Accurate and fast search are two important indicators to measure the quality of image-based retrieval methods. Existing retrieval methods describe the image content by extracting the low-level features of the image, and then use feature comparison to judge whether it is a similar image. In order to improve the accuracy of the search, the extracted image features are often hundreds or thousands of dimensions. When the image library reaches hundreds of thousands or...

AI Technical Summary

Problems solved by technology

The technical problem to be solved by the present invention is to solve the problem of large image feature library storage space and slow retrieval speed in mass image retrieval

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