Image retrieval method based on minimum projection errors of multiple hash tables

An image retrieval, multi-hash table technology, applied in special data processing applications, instruments, electrical digital data processing and other directions, can solve the problems of low accuracy, slow retrieval speed, large storage space of image feature library, etc. The effect of short, improved accuracy, and concise hashing

Inactive Publication Date: 2012-06-20
DALIAN UNIV OF TECH
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Problems solved by technology

[0006] The technical problem to be solved by the present invention is to solve the problem of large storage space and slow retrieval speed of the image feature library when retrieving massive images, and to o

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  • Image retrieval method based on minimum projection errors of multiple hash tables
  • Image retrieval method based on minimum projection errors of multiple hash tables
  • Image retrieval method based on minimum projection errors of multiple hash tables

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Embodiment Construction

[0034] The specific embodiments of the present invention will be described in detail below in conjunction with the technical solutions and accompanying drawings.

[0035] Step 1. The image library contains 60,000 color images of 32×32 pixels, 10 categories in total, 6,000 images in each category, which come from the public CIFAR-10 image library. Take out 1000 images as test samples, and the other 59000 images as images to be retrieved. In addition, 8000 images are taken from the 59000 images to be retrieved as training samples. Some images such as figure 2 shown.

[0036] The URL of the image library is: http: / / www.cs.toronto.edu / ~kriz / cifar.html

[0037] Step 2. Convert all color images into grayscale images, and extract 320-dimensional gist features, which mainly describe the texture attributes of images. The feature library to be retrieved and the training feature library are respectively GI={GI 1 , GI 2 ,...,GI 59000}, GI∈R 59000×320 and GT={GT 1 , GT 2 ,..., G...

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Abstract

An image retrieval method based on minimum projection errors of multiple hash tables belongs to the technical field of image retrieval, and is characterized in that the gist features of an image to be retrieved, a training image and a query image are respectively extracted; the principal component direction of training features is calculated and optimized through the iterative quantization method, and features to be retrieved and query features are projected on the optimized principal component direction to acquire the corresponding hash codes; the training features go through energy reduction to get new training features, and the process is repeated until the Num groups of hash codes are acquired; and the Hamming distance between the Num group of hash codes of the query image and the Num group of hash codes of the image to be retrieved is calculated, so that the similarity between the image to be retrieved and the query image can be measured according to the distance. The invention has the effects and benefits that the image retrieval method overcomes the shortcoming that the Hamming spherical radius of a single harsh table is large in case of a high recalling rate, as well as the problem that random projection hashing needs too many hash tables in case of a high recalling rate.

Description

technical field [0001] The invention belongs to the technical field of image retrieval and relates to a content-based image retrieval method, in particular to an image retrieval method based on the minimization of multi-hash table mapping errors. Background technique [0002] Given a query image, the task of image retrieval is to find images similar to it from an image library. The traditional image retrieval method is to represent the image as a high-dimensional Euclidean vector, and use the method of linear scanning image library to search. For a massive image library, the required feature storage space is very large, and the linear search of the image library is very time-consuming. The image hash method greatly reduces the storage space of the feature by encoding the high-dimensional Euclidean feature into a concise binary hash code; at the same time, the approximate nearest neighbor method is used to search for similar images, which effectively improves the search effi...

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Application Information

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IPC IPC(8): G06F17/30
CPCY02D10/00
Inventor 付海燕孔祥维
Owner DALIAN UNIV OF TECH
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