A latent semantic min-Hash-based image retrieval method

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...

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 Parameter Sensitive Hashing, Proc. IEEE int'l Conf. Computer Vision, pp.750-757, 2003.", Restricted Boltzmann Machines (RBMs) method "R .Salakhutdinov, and G.Hinton, Semantic Hashing, SIGIR workshop on Information Retrieval and Applications of Graphical Models, 2007.", Kernel Hashing (KSH) method "W.Liu, J.Wang, R.Ji, Y.Jiang, and S.Chang, Supervised Hashing with Kernels, in Proc.IEEE Conf.ComputerVision 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, SequentialProjection Learning for Hashing with Compact Codes, in Proc.IEEE Conf.Int'lConf.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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