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An Image Retrieval Method Based on Latent Semantic Minimal Hash

An image retrieval and potential technology, applied in the field of image processing, can solve the problems of reduced accuracy, large training time consumption, insufficient retrieval accuracy, etc., and achieve the effect of improving accuracy

Active Publication Date: 2021-03-19
XI'AN INST OF OPTICS & FINE MECHANICS - CHINESE ACAD OF SCI
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AI Technical Summary

Problems solved by technology

[0009] In order to overcome the defect of insufficient retrieval accuracy of unsupervised methods, researchers have proposed supervised methods and semi-supervised methods that use labeled samples for training to construct hash functions. The typical supervised hash method is the BoostSSC method "G.Shakhnarovich, P.Viola, and T.Darrell, Fast Pose Estimation with ParameterSensitive Hashing, Proc. IEEE int'l Conf. Computer Vision, pp.750-757, 2003.", Restricted Boltzmann Machines (RBMs) method "R .Salakhutdinov, and G.Hinton, Semantic Hashing, SIGIRworkshop on Information Retrieval and Applications of Graphical Models, 2007.", Kernel Hashing Method (KSH) Method "W.Liu, J.Wang, R.Ji, Y.Jiang, and S.Chang, Supervised Hashing with Kernels, in Proc.IEEE Conf.Computer Vision and Pattern Recognition,pp.2074-2081,2012."; semi-supervised hashing method stands for semi-supervised compact hashing (S3PLH) method "J. Wang, S.Kumar and S.Chang, Sequential Projection Learning for Hashing with CompactCodes, in Proc.IEEE Conf.Int'l Conf.on Machine Learning,pp.3344-3351,2010.", and semi-supervised hashing SSH method “J.Wang, S.Kumar, and S.Chang, “Semi-Supervised Hashing for Scalable Image Retrieval,” in Proc.IEEE Conf.Computer Vision and Pattern Recognition,pp.3424-3431,2010.”
For the supervised and unsupervised hash index methods, although the accuracy of the retrieval system is improved, on the massive image library, due to the need to label the samples and the training takes a lot of training time, if the label information of the image is wrong or has been maliciously modified, the accuracy of retrieval will also be reduced

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  • An Image Retrieval Method Based on Latent Semantic Minimal Hash
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  • An Image Retrieval Method Based on Latent Semantic Minimal Hash

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Embodiment Construction

[0047] refer to figure 1 , the steps that the present invention realizes are as follows:

[0048] Step 1, divide the training sample set and test sample set.

[0049] (1a) divide the data set image into a training sample set and a test sample set, when dividing the sample set, randomly extract 10% of the image set as a test sample set, and the remaining images as a training sample set;

[0050] (1b) The pictures in the training set images also serve as a database for subsequent queries.

[0051] Step 2, build a minimal hash model based on latent semantics.

[0052] (2a) For all image sets, including training set images and test set images, use the convolutional network model trained by K.Chatfield et al. in "Return of the Devil in the Details: Delving Deep into Convolutional Nets" to extract images Convolutional network features, and do L on the extracted features 2 standardization;

[0053] (2b) After extracting all the features of the entire image data set, centralize t...

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Abstract

The invention relates to the technical field of image processing and in particular relates to a latent semantic min-Hash-based image retrieval method comprising the steps of (1) obtaining datasets through division; (2) establishing a latent semantic min-Hash model; (3) solving a transformation matrix T; (4) performing Hash encoding on testing datasets Xtest; (5) performing image query. Based on the facts that the convolution network has better expression features and latent semantics of primitive characteristics can be extracted by using matrix decomposition, minimizing constraint is performed on quantization errors in an encoding quantization process, so that after the primitive characteristics are encoded, the corresponding Hamming distances in a Hamming space of semantically-similar images are smaller and the corresponding Hamming distances of semantically-dissimilar images are larger. Thus, the image retrieval precision and the indexing efficiency are improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image retrieval technology, which can be used in the fields of retrieval management of large-scale commodity images and image search engines and other fields of searching images by images. Background technique [0002] In the era of Web 2.0, especially with the popularity of social networking sites such as Flickr and Facebook, heterogeneous data such as images, videos, audios, and texts are growing at an alarming rate every day. For example, as of December 2014, the image-sharing website Flick has uploaded 4.25 billion pictures in total, and Facebook has more than 1 billion registered users, and more than 1 billion pictures are uploaded every month. How to better establish an effective retrieval mechanism to realize convenient, fast and accurate query and retrieval of the image information required by users in the vast image database has become an urgent p...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/583G06K9/62
Inventor 李学龙卢孝强袁勇
Owner XI'AN INST OF OPTICS & FINE MECHANICS - CHINESE ACAD OF SCI
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