CN104298791A - Rapid image retrieval method based on integrated Hash encoding - Google Patents
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Abstract
The invention discloses a rapid image retrieval method based on integrated Hash encoding, belongs to the technical field of digital image retrieval. The rapid image retrieval method comprises the following steps: firstly, extracting a training image and checking the SIFT characteristics of the image, and performing initial Hash encoding on the training image by using M Hash algorithms; subsequently relearning the initial Hash encoding result by using a consistency constraint rule in integrated learning, so as to obtain an integrated Hash mapping matrix; and finally performing integrated Hash encoding on the training image and the checking image again, and performing rapid retrieval by calculating the Hamming distance between the checking image and the training image on the basis of the integrated Hash encoding. According to the integrated Hash encoding used in the method, the characteristics and advantages of different Hash algorithms can be combined simultaneously, the problem that a single Hash algorithm is insufficient in judgment power and the application range is limited are solved, and thus rapid retrieval of images is relatively accurate and efficient.
Description
Technical field
The present invention relates to CBIR method, particularly a kind of fast image retrieval method of encoding based on integrated Hash, belongs to digital image search technical field.
Background technology
Along with the development of multimedia network technology, the image resource on internet is explosive growth, makes user be difficult in vast as the open sea data, find real interested information.Therefore, how the image of magnanimity analyzed fast and effectively and retrieved into a very challenging task.Traditional CBIR technology is all often that the low-level image feature by extracting image carries out exhaustive comparison, but the time complexity because of its comparison procedure is linear, cannot carry out expanding and applying in large scale network view data, and due to the bottom visual signature thousands of dimension easily of image, many image retrieval application also can run into the problem of dimension disaster, and how to store so huge raw data is also a huge bottleneck.
In 10 years of past, the fast searching techniques of researchers to image studies in detail.Wherein, the image search method based on Hash coding achieves immense success.Such algorithm completes approximate neighbor search by binary coding vector image table being shown as low-dimensional.The approximate neighbor search utilizing binary coding to carry out image is extremely fast, because: 1) coding vector of image is high compression, it all can be loaded among internal memory; 2) Hamming (Hamming) distance between coding just can be obtained by the xor operation of step-by-step, and therefore this computation process is very efficiently (desktop computer that nowadays, a Daepori leads to just can complete the calculating of millions of Hamming distance within several milliseconds).
At present, traditional image Hash encryption algorithm is mainly divided into non-data to rely on and data dependence two schemes.Wherein a kind of very famous non-data relies on hash algorithm is local sensitivity Hash (Locality Sensitive Hashing, LSH), but the randomness of its projection vector causes its code efficiency not high, it often needs to build multiple Hash tables with very long codes length could obtain ideal effect.In recent years, research emphasis is transferred on the hash algorithm of data dependence by researchers, attempt to replace accidental projection to find better data dependence hash function by the method for machine learning, as restriction Boltzmann machine (the Restricted Boltzmann Machines based on degree of depth learning network, RBMs) with based on spectrum Hash (Spectral Hashing, the SH) algorithm etc. of spectrogram segmenting.RBMs algorithm successively carries out dimensionality reduction and study to the primitive character of image by neural network model, and finally obtains a binary coding of compacting.SH algorithm carries out Hash coding by building Laplce's characteristic pattern and utilizing principal component analysis (PCA) (PCA) method to extract its proper vector to original image.But said method is all carry out Hash coding based on single image bottom visual signature information, the content information that image is abundant cannot be expressed all sidedly.Follow-up research, as multi-feature Hash algorithm (Multiple Feature Hashing, and multi-source information synthesis hash algorithm (Composite Hashing with Multiple Information Sources MFH), CHMIS) etc., the expansion of in the fusion etc. of various features information, data dependence hash algorithm being correlated with again.Although above-mentioned hash algorithm proposes for the different angles of problem, all there is respective Some features and superiority, they often only on some specific setting model or database effectively, scope extendability is poor.In addition, in view of the diversity of network image data characteristics and the complicacy of structure, traditional image search method of encoding based on single Hash cannot meet the demand for the accuracy rate of rapid image retrieval in practical application at present.
Summary of the invention
The object of the invention is to overcome the deficiencies in the prior art, propose a kind of fast image retrieval method of encoding based on integrated Hash.Feature of the present invention is the feature that can make full use of different hash algorithm, merge the Encoder Advantage of different hash algorithm, more effectively carried out the study of integrated Hash coding by consistency constraint criterion, thus improve accuracy rate and the universality of fast image retrieval method.
Technical scheme of the present invention is: extract its SIFT feature respectively to the training image in database and query image and utilize K-mean cluster that its quantization table is shown as the form of proper vector, different image Hash encryption algorithms is utilized to carry out initial Hash coding to the proper vector of training image, then the similarity matrix in the initial Hash basis of coding obtained in algorithms of different respectively between calculation training image, and obtain new integrated Hash mapping matrix by the consistency constraint criterion study of Ensemble Learning Algorithms, the proper vector of integrated Hash mapping matrix to all training images and query image is finally utilized to re-start integrated Hash coding, and quick-searching is carried out by the Hamming distance calculated between query image and training image on the basis that integrated Hash is encoded.Its concrete steps are as follows:
(1) data-oriented storehouse, be divided into training image database and query image database, respectively SIFT feature is extracted to each width training image and query image and utilizes the proper vector that its quantization means is tieed up for d by K-mean cluster, wherein n and q is respectively the quantity of training image and query image, then the proper vector of all training images can form a training image proper vector storehouse, wherein X is the matrix of dimension, every a line of X is respectively the proper vector of corresponding training image, the proper vector of all query image can form a query image proper vector storehouse, wherein Y is the matrix of dimension, every a line of Y is respectively the proper vector of respective queries image.
(2) choose the proper vector storehouse X of existing M kind different images hash algorithm to training image and carry out initial Hash coding respectively, the initial Hash encoder matrix obtained is designated as (m=1 respectively, M), wherein, being a dimension is, element value is the matrix of-1 or 1, every a line represents the initial Hash coding of a width training image, and n is training image sum, is the code length of m kind hash algorithm.
(3) from the proper vector storehouse X of training image, selecting k width image by row obtains a submatrix at random, dimension is, from each initial Hash encoder matrix, distinguish random selecting submatrix by row accordingly simultaneously, dimension is, on the initial Hash coded sub-matrices basis of each hash algorithm, by the inner product of vector calculate two width training images initial Hash coding between similarity, the training image wherein under m kind hash algorithm initial Hash coding between calculating formula of similarity be:
It is wherein the initial Hash coding similarity matrix of the training image under m kind hash algorithm, dimension is, in each element representation i-th width training image and the similarity numerical value of jth width training image under m kind hash algorithm, and, value larger expression two width image initial Hash coding more similar, otherwise then more dissimilar, represent transpose of a matrix.
(4) according to the training image under the different hash algorithm of formulae discovery M kind below initial Hash coding between average similarity:
Wherein for having merged the average similarity matrix of the initial Hash coding similarity of the different hash algorithm of M kind, dimension is.
(5) on the basis of average similarity matrix S, the consistency constraint criterion in Ensemble Learning Algorithms is utilized to carry out learning again of image Hash coding, so-called consistency constraint criterion refer to by the integrated Hash learning again to obtain encode the similarity that calculates will the average similarity matrix S-phase of hash algorithm different from M kind consistent, detailed process is by minimizing objective function realization below:
Wherein for learning the integrated Hash encoder matrix obtained, every a line represents the integrated Hash coding of a width training image, and be the length that integrated Hash is encoded, if integrated Hash coding adopts the form of linear mapping, then above-mentioned objective function can be written as:
Be wherein integrated Hash mapping matrix, dimension is, effect is that image is mapped to Hamming space from feature space, and sign (.) is for getting sign function.
(6) for the proper vector of any width query image in query image proper vector storehouse, integrated Hash mapping matrix is utilized to be mapped to Hamming space, obtain its integrated Hash coding, integrated Hash coding is re-started to the proper vector storehouse X of training image simultaneously, calculate and the integrated Hash of each width training image encode between Hamming distance, if the value of Hamming distance is less than threshold value, then this width training image is returned to user as the similar image of corresponding query image.
Effect of the present invention and benefit are: the present invention proposes a kind of fast image retrieval method of encoding based on integrated Hash, merge information and the feature of multiple different images Hash encryption algorithm simultaneously, the study of integrated Hash mapping matrix is carried out by the consistency constraint criterion of Ensemble Learning Algorithms, more effectively can carry out integrated Hash coding to image, solve traditional algorithm problem that judgement index is not enough and universality is poor in rapid image retrieving of encoding based on single Hash, thus the accuracy rate of rapid image retrieval can be improved.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of fast image retrieval method of encoding based on integrated Hash of the present invention.
Fig. 2 is the comparison diagram of the rapid image result for retrieval of the present invention and LSH algorithm, SH algorithm and RBMs algorithm, the query image that wherein the first behavior two width is different, below every a line first three width retrieving images result of being respectively different algorithm and returning.
Embodiment
Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:
As shown in Figure 1, the invention discloses a kind of fast image retrieval method of encoding based on integrated Hash, comprise following steps:
(1) the NUS-WIDE network image database disclosed in the NUS of the database source in the embodiment of the present invention, the view data in image library, from famous photo sharing website Flickr, wherein comprises 269648 width images altogether.Therefrom random selecting 2000 width image is as query image database, all the other 267648 width image composition training image databases.Then the SIFT feature of each width training image and query image is extracted respectively, and utilize K-mean cluster to be the proper vector of 500 dimensions by its quantization means, thus obtain training image proper vector storehouse X and query image proper vector storehouse Y respectively, wherein X to be dimension be 267648500 matrix, Y to be dimension be 2000500 matrix.
(2) the image hash algorithm (LSH algorithm, RBMs algorithm and SH algorithm) utilizing M=3 kind different carries out initial Hash coding to the proper vector storehouse X of training image respectively, code length is, the initial Hash encoder matrix obtained is respectively,, matrix dimensionality is all 26764864.Wherein these three kinds of image hash algorithms can be realized by source code disclosed on network.
(3) from the proper vector storehouse X of training image, width image is chosen by row to form a submatrix at random, its dimension is 10000500, simultaneously accordingly from each initial Hash encoder matrix, middle choose by row respectively a submatrix,, dimension is 1000064, similarity matrix between the initial Hash coding then calculating training image under 3 kinds of different hash algorithms respectively,, computing formula is as follows:
Dimension is wherein 1000010000, each element representation width training image and width training image similarity numerical value under m kind initial Hash is encoded, span is-1 to 1.
(4) similarity matrix and is afterwards obtained, the average similarity according between the training image initial Hash coding under formulae discovery 3 kinds of different hash algorithms below:
Wherein for having merged the average similarity matrix of the initial Hash coding similarity of 3 kinds of different hash algorithms, dimension is 1000010000.
(5) on the basis of average similarity matrix S, the consistency constraint criterion of Ensemble Learning Algorithms is utilized to carry out learning again of image Hash coding, the integrated Hash coding that study is obtained has advantage and the feature of LSH algorithm, RBMs algorithm and SH algorithm simultaneously, and detailed process is realized by the objective function minimized below:
Be wherein the length of integrated Hash coding, this get=64, for integrated Hash encoder matrix, every a line represents the integrated Hash coding of a width training image, dimension is, solves obtain by carrying out gradient descent method to objective function, integrated Hash coding is adopted the form of linear mapping, that is, then above-mentioned objective function is written as:
Wherein sign (.) is for getting sign function, and be integrated Hash mapping matrix, dimension is 50064, and effect is that image is mapped to Hamming space from feature space, to be solved draw by the mode of random initializtion via gradient descent method.
(6) for any width query image in 2000 width query image, integrated Hash mapping matrix is utilized to map its proper vector, obtain its integrated Hash coding, integrated Hash coding is re-started to the proper vector storehouse of all training images simultaneously, calculate respectively and the integrated Hash of each width training image encode between Hamming distance, if Hamming distance is less than threshold value, then this width training image is returned to user as the similar image of query image.In this embodiment, get.
See Fig. 2, the comparison diagram of the rapid image result for retrieval of the present invention and LSH algorithm, SH algorithm and RBMs algorithm.The two width query image randomly drawed in all query image of the first behavior in Fig. 2, the result for retrieval of first three width similar image that the algorithm that every a line is respectively different below returns, wherein correct result for retrieval is with to labelled notation, the result for retrieval wrong number of mistake marks, and visible method of the present invention can obtain than other three kinds of single image hash algorithms result for retrieval more accurately.
Above-described specific embodiments has carried out further detailed description to object of the present invention and technical scheme; know and it should be appreciated by those skilled in the art that; the foregoing is only specific embodiment of the invention scheme; and be not used to limit scope of the present invention; do not departing from the situation of being defined by the appended claims; various amendment can be carried out in detail, all should be considered as belonging to protection scope of the present invention.
Claims (1)
1., based on the fast image retrieval method that integrated Hash is encoded, comprise the following steps:
(1) data-oriented storehouse, be divided into training image database and query image database, respectively SIFT feature is extracted to each width training image and query image and utilizes the proper vector that its quantization means is tieed up for d by K-mean cluster, wherein n and q is respectively the quantity of training image and query image, then the proper vector of all training images can form a training image proper vector storehouse, wherein X is the matrix of dimension, every a line of X is respectively the proper vector of corresponding training image, the proper vector of all query image can form a query image proper vector storehouse, wherein Y is the matrix of dimension, every a line of Y is respectively the proper vector of respective queries image,
(2) choose the proper vector storehouse X of existing M kind different images hash algorithm to training image and carry out initial Hash coding respectively, the initial Hash encoder matrix obtained is designated as (m=1 respectively, M), wherein, being a dimension is, element value is the matrix of-1 or 1, every a line represents the initial Hash coding of a width training image, and n is training image sum, is the code length of m kind hash algorithm;
(3) from the proper vector storehouse X of training image, selecting k width image by row obtains a submatrix at random, dimension is, from each initial Hash encoder matrix, distinguish random selecting submatrix by row accordingly simultaneously, dimension is, on the initial Hash coded sub-matrices basis of each hash algorithm, by the inner product of vector calculate two width training images initial Hash coding between similarity, the training image wherein under m kind hash algorithm initial Hash coding between calculating formula of similarity be:
It is wherein the initial Hash coding similarity matrix of the training image under m kind hash algorithm, dimension is, in each element representation i-th width training image and the similarity numerical value of jth width training image under m kind hash algorithm, and, value larger expression two width image initial Hash coding more similar, otherwise then more dissimilar, represent transpose of a matrix;
(4) according to the training image under the different hash algorithm of formulae discovery M kind below initial Hash coding between average similarity:
Wherein for having merged the average similarity matrix of the initial Hash coding similarity of the different hash algorithm of M kind, dimension is;
(5) on the basis of average similarity matrix S, the consistency constraint criterion in Ensemble Learning Algorithms is utilized to carry out learning again of image Hash coding, so-called consistency constraint criterion refer to by the integrated Hash learning again to obtain encode the similarity that calculates will the average similarity matrix S-phase of hash algorithm different from M kind consistent, detailed process is by minimizing objective function realization below:
Wherein for learning the integrated Hash encoder matrix obtained, every a line represents the integrated Hash coding of a width training image, and be the length that integrated Hash is encoded, if integrated Hash coding adopts the form of linear mapping, then above-mentioned objective function can be written as:
Be wherein integrated Hash mapping matrix, dimension is, effect is that image is mapped to Hamming space from feature space, and sign (.) is for getting sign function;
(6) for the proper vector of any width query image in query image proper vector storehouse, integrated Hash mapping matrix is utilized to be mapped to Hamming space, obtain its integrated Hash coding, integrated Hash coding is re-started to the proper vector storehouse X of training image simultaneously, calculate and the integrated Hash of each width training image encode between Hamming distance, if the value of Hamming distance is less than threshold value, then this width training image is returned to user as the similar image of corresponding query image.
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Application publication date: 20150121 |