Image Retrieval Method Based on Local Preserving Iterative Quantized Hashing

Abstract

The invention discloses an image retrieval method based on local locality preserving iterative quantization hash and mainly solves the problems that large-scale image retrieval has high memory usage rate and low retrieval performance. The method includes the steps of 1, extracting and normalizing original image features; 2, subjecting normalization data to principal components analysis to obtain a low-dimensional normalization data matrix; 3, dividing low-dimensional normalization data into training data and testing data; 4, construction a neighbor graph of image training data matrixes to obtain neighborhood relation of the image training data; 5, with the neighborhood relation as a constraint, updating a rotation matrix by iterative quantization; 6, acquiring a hash code of the image training data and that of the image testing data according to the rotation matrix; and 7, acquiring retrieval results according to a Hamming distance between the hash code of the training data and that of the testing data. The method has the advantages that memory consumption is lowered, image retrieval performance is improved, and the method is applicable to image retrieval services for mobile equipment, the internet of things and E-commerce.

Description

technical field [0001] The invention belongs to the field of image processing, and further relates to a fast retrieval method for large-scale image data, which can be used for image search services of mobile devices, the Internet of Things, e-commerce, and the like. Background technique [0002] In recent years, with the rapid development of the Internet, cloud computing, mobile devices, and the Internet of Things, the amount of global data has entered the ZB era and is still growing exponentially every year. Image data in big data is the same as human visual cognition, and plays an important role in information expression. How to efficiently retrieve valuable images to make full use of big data to gain benefits has become a major problem today. In order to efficiently retrieve valuable images in big data, people propose hashing algorithms. This algorithm can convert an image into a binary code sequence of a certain length. Since the binary code sequence can be directly st...

AI Technical Summary

Problems solved by technology

However, the disadvantages of the method proposed in this patent application are: on the one hand, the principal component direction is obtained by linear dimensionality reduction, which does not conform to the nonlinear characteristics of most data in reality, and the subsequent iterative optimization of the principal component direction process will be Increase the time complexity of the algorithm; on the other hand, for big data, the use of multi-hash tables increases the occupancy rate of storage space

However, the premise of this method is that the image data obeys the Gaussian distribution, but the actual data may not obey the Gaussian distribution, and this method does not consider the neighbor relationship between images, which reduces the retrieval performance

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