Image Hash retrieval method based on KPCA multi-table index

A table index and image technology, applied in the fields of content-based image retrieval and nearest neighbor search, can solve the problems of low discrimination, difficulty in ensuring retrieval accuracy, and difficulty in meeting efficiency requirements for large-scale data linear search, and achieves improved recall. rate effect

Active Publication Date: 2017-06-09
FUZHOU UNIV
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Problems solved by technology

However, there must be two important problems in this way: 1) image features are often a kind of high-dimensional data, high-dimensional data storage requirements are high, and the computational efficiency and class-to-class distinction are low; 2) It is difficult to meet the efficiency requirements for linear search on large-scale d

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  • Image Hash retrieval method based on KPCA multi-table index
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  • Image Hash retrieval method based on KPCA multi-table index

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[0035] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments.

[0036] The invention provides a KPCA-based multi-table index image hash retrieval method. Before training the hash coding function, feature extraction is performed on original high-dimensional features, and the KPCA method is used to obtain highly distinguishable features as a training set. It specifically includes the following steps: Step S1: Extract the original high-dimensional features, and use KPCA to obtain highly distinguishable features as a training set; Step S2: Use the improved k-means clustering algorithm to calculate any two sample point feature vector x i and x j The Euclidean distance between dis(x i ,x j ), find the two farthest sample features c 0 and c 1 , calculate c 0 and c 1 middle point c 2 , and take these three sample points as the initial cluster centers. Perform clustering and quantification processing on the...

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Abstract

The invention provides an image Hash retrieval method based on the KPCA multi-table index. The method includes the steps of distinguishing feature selection, feature clustering, multi-table index construction and Hash code optimization. According to the method, before a Hash projection function is trained, firstly, features with the distinguishing capability are selected from image feature dimensions through principal component analysis based on a kernel function to serve as a training set; on this basis, clustering centers of different semantic samples are acquired through a feature clustering method, and multiple optimal neighboring clusters of each cluster are found; finally, hierarchical division is conducted on a clustering space to construct multiple index tables. In retrieval, multiple Hash index tables are searched to improve the retrieval performance. In the method, the high-dimensional image features are mapped into simple binary codes, and thus the data storage space is saved; the problem that when a single-table index structure is adopted, similar images have a large dispersion difference or similar features have a large attribute distribution interval, in other words, original similar features are mapped to different Hash codes is solved.

Description

technical field [0001] The invention belongs to the field of image retrieval, relates to a content-based image retrieval method, and is suitable for large-scale image retrieval and nearest neighbor search of high-dimensional data. Background technique [0002] The image database management system in the 1970s manually marked the semantic content of the image, and used traditional database technology or text information retrieval technology to store and index the semantic keywords of similar images. Its advantage is that it is based on mature database retrieval technology and text content indexing technology, and the retrieval speed is relatively ideal. However, web pages are text information associated with images rather than feature information related to image content, resulting in partial index results that do not meet user requirements, and with the advent of the era of big data, the scale of image data has grown exponentially. This subjective and inconsistent manual la...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/51G06F16/583
Inventor 郭太良叶芸林志贤林金堂邓清文
Owner FUZHOU UNIV
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