Ultra low complexity image retrieval method based on sequence preserving hashing

An image retrieval and complexity technology, applied in special data processing applications, instruments, electrical digital data processing, etc., to achieve the effect of efficient hash coding mechanism, improve accuracy, and reduce complexity

Active Publication Date: 2017-05-31
XIAMEN UNIV
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  • Application Information

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Problems solved by technology

[0007] The purpose of the present invention is to solve the previous unsupervised hash learning algorithm, the training model is to limit the feature measurement space, that is, the usual model is to carry out model learning and optimization in the Euclidean space, in order to deal with and deal with

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  • Ultra low complexity image retrieval method based on sequence preserving hashing
  • Ultra low complexity image retrieval method based on sequence preserving hashing
  • Ultra low complexity image retrieval method based on sequence preserving hashing

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Embodiment Construction

[0040] The following embodiments will describe the present invention in detail with reference to the accompanying drawings.

[0041] Take the CIFAR10 data as an example for illustration. CIFAR10 contains 60,000 images of size 32×32. The pictures can be divided into 10 categories in total, such as airplanes, flowers, etc.

[0042] See Table 1 for the average accuracy index values ​​corresponding to different hash algorithms in the CIFAR10 dataset.

[0043] Table 1

[0044]

[0045] The present invention comprises the following steps:

[0046] 1) For the images in the image library, randomly select a part of the images as the training set, and extract the corresponding image features, the image features include but not limited to GIST features (you can refer to the article Aude Oliva and AntonioTorralba, "Modeling the Shape of the Scene : A Holistic Representation of the Spatial Envelope”, in the International Journal of Computer Vision);

[0047] 2) Using the nonlinear ...

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Abstract

The invention discloses an ultra low complexity image retrieval method based on sequence preserving hashing, and relates to image retrieval. For images in an image library, a part of images are selected randomly to serve as a training set, and corresponding image features are extracted; the dimension of an original image feature is reduced to the length same as a hash code through a nonlinear principal component analysis method; a series of supporting points are obtained through a K-means clustering algorithm to serve as the basis of follow-up hash function learning; a corresponding hash function is learned through iterative optimization; the corresponding hash function is output, and a hash code of the whole image library is calculated; for a querying image, a corresponding GIST feature is extracted, hash coding is conducted on the image feature according to the hash code function obtained through training, the hamming distance between the hash code of the querying image and an image feature code in the image library is calculated, the similarity between the querying image and a to-be-retrieved image in the image library is measured through the hamming distance, and the image high in similarity is returned.

Description

technical field [0001] The invention relates to image retrieval, in particular to an ultra-low-complexity image retrieval method based on sequence-preserving hash. Background technique [0002] With the development of Internet technology, the amount of image data in the network is increasing exponentially. How to efficiently organize, manage and analyze these data is very important. Content-Based Image Retrieval (Content Based Image Retrieval, CBIR) technology emerges at the historic moment, and has received extensive attention from academia and industry. Generally, the CBIR system can be divided into two parts: 1. Image feature expression; 2. Efficient retrieval algorithm. [0003] The feature expression of the image is to extract the relevant features of the image to describe the content of the image, such as VLAD features (for details, please refer to the article H, Jegou, M.Douze, C. Schmid and P.Perez "Aggregating local descriptors into a compact image representation ...

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Application Information

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IPC IPC(8): G06F17/30G06K9/46G06K9/62
CPCG06F16/583G06V10/44G06F18/23213
Inventor 纪荣嵘林贤明刘弘
Owner XIAMEN UNIV
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