CN101021849A - Transmedia searching method based on content correlation - Google Patents

Abstract

This invention discloses a method for media-crossing searches based on content relativity, which applies the typical relativity analysis to analyze the content characters of different mode media data, maps a visual sense character vector of image data and an auditory character vector of audio data in a low dimension isomorphic sub-space simultaneously by a sub-space mapping algorithm, measures the relativities among different mode data based on a general distance function and modifies the topological structure of a multi-mode data set in the sub-space to increase the cross media search efficiency effectively.

Description

Content-based correlativity stride the medium search method

Technical field

The present invention relates to multimedia retrieval, what relate in particular to a kind of content-based correlativity strides the medium search method.

Background technology

Content-based multimedia retrieval is the research focus of computer vision and information retrieval field, carries out the similarity coupling according to vision, the sense of hearing or several how low-level image feature and realizes retrieval.As far back as 1976, Mai Geke just disclosed human brain to external world the cognition of information need cross over and comprehensive different sensory information, with the understanding of formation globality.The research of cognitive neuropsychology aspect has in the recent period verified further that also the human brain cognitive process presents the characteristic of striding medium, produces cognitive result from the information stimulation mutually of different sense organs such as vision, the sense of hearing, acting in conjunction.Therefore, press at present research a kind of support different modalities stride the medium search method, break through the restriction that the content-based multimedia retrieval of tradition only acts on the single mode data.

The content-based medium retrieval technique of striding is meant by the low-level image feature to multimedia object and analyzes, be implemented in the leap from a kind of mode to another kind of mode in the retrieving, it is the inquiry example that the user submits a kind of mode to, system returns the multimedia object of other different modalities similarly, has broken through the restriction to single mode of the image retrieval, audio retrieval, three-dimensional picture retrieval etc. of single mode.Stride new research field in multimedia analysis that medium retrievals is based on content and the retrieval, also ripe without comparison in the world at present medium searching algorithm and the technology of striding.

The initial stage nineties, people proposed the CBIR technology, extracted the visual signature of bottom from image, such as the index of bottom visual signatures such as color, texture, shape as image.This technology also was applied to video frequency searching and audio retrieval afterwards, and wherein also different at the low-level image feature that different media content adopted, video frequency searching may be used the motion vector feature, and audio retrieval is used time domain, frequency domain, compression domain feature etc.It is the prototype system of representative that content-based multimedia retrieval method has with QBIC, VideoQ etc. in early days, but owing to lack the support of high-level semantic, can not satisfy user's requirement on accuracy rate and efficient; Methods such as example study afterwards, convergence analysis and manifold learning are used to realize semantic information of multimedia understanding, to fill up the wide gap between low-level image feature and the high-level semantic; Then in order to overcome the deficiency of training sample, relevant feedback mechanism often is used, with perception priori in conjunction with the user, for example: utilize feedback information to revise query vector and make its distribution center to the coordinate indexing object move, adjust the weights etc. of each component in the distance metric formula, some machine learning methods also combine with related feedback method recently.Semantic wide gap has been dwindled in the use of these methods to a certain extent, has improved the performance of single mode retrieval.

Yet, the multimedia database that comprises single mode all can only be retrieved by existing multimedia retrieval system, though or can handle multi-modal media data, do not support to stride the retrieval of medium, promptly retrieve the multimedia object of other mode according to a kind of multimedia object of mode.Because not only intrinsic dimensionality is different between the aural signature of visual feature of image and audio frequency, and expresses different attributes, can't directly measure similarity, this isomerism and noncomparabilities are present between the multi-medium data of other mode equally.Therefore, above-mentioned single mode search method all can not be directly used in strides medium retrievals, because different with the single mode retrieval, the research object of striding the medium retrieval is different modalities, the low-level image feature space of isomery each other.

Some researchers have successively proposed similar research of striding medium thought, for example carry out the index and the retrieval of video database by excavating multi-modal feature, the text that transcribed text that news-video comprised and internet page are comprised is analyzed, realized object video and the similar coupling of internet page on text feature.But these researchs are at low-level image features different in the modality-specific media object, for example: the transcribed text that comprises in the video clips, color, texture etc., can not realize the flexible leap between the different modalities media data.

Canonical correlation analysis (Canonical Correlation Analysis) is a kind of statistical analysis technique, is applied to the data analysis of aspects such as economy, medical science, meteorology the earliest.But aspect multi-medium data analysis and retrieval, canonical correlation analysis but seldom is used, because this statistical analysis technique is to analyze the correlation information that exists between two kinds of different variablees fields, and traditional single mode retrieval technique research is a kind of single feature space of mode.

Summary of the invention

The present invention overcomes above-mentioned existing method in the restriction of retrieval on the mode, and what a kind of content-based correlativity was provided strides the medium search method.

The medium search method of striding of content-based correlativity may further comprise the steps:

(1) gathers the object of different modalities from multimedia database: image and voice data;

(2) visual signature of extraction view data, and the aural signature of voice data, vision that the extraction of employing canonical correlation analysis obtains and the canonical correlation between the aural signature;

(3) adopt isomorphism subspace mapping algorithm, the visual feature vector of view data and the aural signature vector of voice data are mapped in the isomorphism subspace of a low-dimensional simultaneously, realize the unified expression of different modalities media data;

(4) adopt polar mode to define general distance function, the correlativity size between tolerance different modalities media data, and stride the medium retrieval on this basis;

(5), be used for extracting the priori of user interactions, to revise the topological structure of multi-medium data collection in the isomorphism subspace based on the relevant feedback mechanism of incremental learning;

(6),, other media object beyond the training set are accurately navigated in the isomorphism subspace perhaps by relevant feedback mechanism according to the base vector of asking in the mapping process of subspace.

The visual signature of described extraction view data, and the aural signature of voice data, vision that the extraction of employing canonical correlation analysis obtains and the canonical correlation between the aural signature: the level image visual signature constitutes the characteristics of image vector of p dimension, the bottom aural signature of audio frequency constitutes q dimension audio feature vector, adopts canonical correlation analysis to learn visual feature of image X simultaneously _{(n * p)}Aural signature Y with audio frequency _{(n * q)}, the eigenmatrix X of isomery _{(n * p)}And Y _{(n * q)}Between related coefficient be calculated as follows:

$\mathrm{\ρ}=r(L,M)=\frac{A^T{C}_{\mathrm{xy}}B}{\sqrt{A^T{C}_{\mathrm{xx}}A{B}^T{C}_{\mathrm{yy}}B}},$ $(C_{\mathrm{xy}}=\left[\begin{array}{cc}{C}_{\mathrm{xx}}& C_{\mathrm{xy}}\\ C_{\mathrm{yx}}& C_{\mathrm{yy}}\end{array}\right]=\mathrm{C)}---1$

Wherein A and B are linear transformation, by formula 2 turn to relevant between less union variable L and M having relevant between the eigenmatrix X of more a plurality of variablees and the Y, the numeric distribution of A and B is determined the space correlation distribution form of X and Y, the numerical values recited of A and B determine to the significance level of dependent variable.

Adopt isomorphism subspace mapping algorithm, the visual feature vector of view data and the aural signature vector of voice data are mapped in the isomorphism subspace of a low-dimensional simultaneously, realize the unified expression of different modalities media data: isomorphism subspace mapping algorithm is on the basis of canonical correlation analysis, study obtains the low n-dimensional subspace n of an optimum, has farthest kept original feature vector X _{(n * p)}And Y _{(n * q)}Between correlativity, algorithm steps is as follows:

Input: image characteristic matrix X _{(n * p)}, audio frequency characteristics matrix Y _{(n * q)}

Output: all images data and the voice data vector representation L in low n-dimensional subspace n _{(n * m)}And M _{(n * m)}

Step 1:, view data all in the database and voice data are divided into different semantic classess with the average cluster of K by the mode of semi-supervised learning;

Step 2: under the constraint of formula 3, make related coefficient ρ=r (L, M) optimization,

v(L)＝L ^TL＝A ^TX ^TXA＝1；v(M)＝M ^TM＝B ^TY ^TYB＝1 3

Adopt method of Lagrange multipliers to obtain the equation C that form is Ax=λ Bx _XyC _Yy ^-1C _YxA=λ ²C _XxA, the characteristic root of asking for this equation promptly obtains separating of matrix A and B;

Step 3: linear method structure isomorphism subspace promptly becomes m dimension coordinate L with B with characteristics of image vector sum audio frequency characteristics DUAL PROBLEMS OF VECTOR MAPPING with base vector A respectively _{(n * m)}And M _{(n * m)}

Adopt polar mode to define general distance function, the correlativity size between tolerance different modalities media data, and stride the medium retrieval on this basis: image and voice data in the m n-dimensional subspace n with polar formal definition proper vector x _i'=(x _I1' ..., x _Ik' ..., x _Im'), (x _Ik'=a+bi, (a, b ∈ R)), between image and the image, between audio frequency and the audio frequency and the similarity between image and the voice data be calculated as follows with general distance function:

${\mathrm{CCAdis}}_{({x_i}^{\′},{x_j}^{\′})}=\mathrm{sqrt}\underset{k=1}{\overset{m}{\mathrm{\Σ}}}({\left|{x_{\mathrm{ik}}}^{\′}\right|}^2+{\left|{x_{\mathrm{jk}}}^{\′}\right|}^2-2\×|{x_{\mathrm{ik}}}^{\′}|\×|{x_{\mathrm{jk}}}^{\′}|\×{\mathrm{Cos\θ}}_k);---4$

${\mathrm{\β}}_{\mathrm{ik}}=\mathrm{arctg}(b/a),{\mathrm{\θ}}_k=|{\mathrm{\β}}_{\mathrm{ik}}-{\mathrm{\β}}_{\mathrm{jk}}|,\left|{x_{\mathrm{ik}}}^{\′}\right|=\sqrt{a^2+b^2},k\∈[1,m]$

The user provides inquiry example image by man-machine interface in the retrieving, if this example is in tranining database, then find the m dimension coordinate of inquiry example in the subspace according to the subspace mapping result, with the distance between general distance function calculating and other audio frequency and view data, k image and k the audio frequency nearest with the query image example return to the user as Query Result; Equally, if the inquiry example is a section audio, then retrieve similar audio frequency and image object according to above-mentioned steps.

Relevant feedback mechanism based on incremental learning, be used for extracting the priori of user interactions, to revise the topological structure of multi-medium data collection in the isomorphism subspace: system can commonly use the perception priori that the family provides in relevant feedback process middle school, if Ω presentation video training set, A represents the audio frequency training set, definition " modifying factor " γ _{(i, j)}=Pos (a _i, b _j) (a _i∈ Ω, b _j∈ A), be used to revise similarity between the different modalities media object: Crodis _{(i, j)}=CCAdis _{(i, j)}+ γ _{(i, j)}, modifying factor is initialized as zero;

When the user submits image querying example R to, use CCAdis (i, j) the k neighbour image collection C of calculating R in the subspace ₁, (i j) calculates the k neighbour audio set C of R in the subspace to use Crodis ₂, the return results of striding the medium retrieval is C ₁And C ₂

In user interaction process, the user marks positive example P and negative routine N,  by relevant feedback in Query Result _Pi∈ P, order ${\mathrm{\&gamma;}}_{(R,p_i)}=-\mathrm{\&tau;},(\mathrm{\&tau;}>0),$ And find p according to CCAdis _iIn audio database A-neighbour T={t ₁..., t _j..., t _k), arrange by the ascending order of distance, then in the mode of equal difference, revise the γ value of each element among the set T successively: ${\mathrm{\&gamma;}}_{(R,t_j)}=-\mathrm{\&tau;}+j\&times;{\mathrm{<figure-callout\; id="1"\; label="d"\; filenames="A20061005339000161.png,A20061005339000171.png"\; state="\{\{state\}\}">d</figure-callout>}}_1,({\mathrm{<figure-callout\; id="1"\; label="d"\; filenames="A20061005339000161.png,A20061005339000171.png"\; state="\{\{state\}\}">d</figure-callout>}}_1=\mathrm{\&tau;}/k);$  _Ni∈ N, order ${\mathrm{\&gamma;}}_{(R,n_i)}=\mathrm{\&tau;},(\mathrm{\&tau;}>0),$ And find n according to CCAdis _iK-neighbour H={H in audio database A ₁..., h _j..., h _k, arrange by the ascending order of distance, then in the mode of equal difference, revise the γ value of each element among the set H successively: ${\mathrm{\&gamma;}}_{(R,h_j)}=\mathrm{\&tau;}-j\&times;{\mathrm{<figure-callout\; id="2"\; label="d"\; filenames="A20061005339000161.png,A20061005339000171.png"\; state="\{\{state\}\}">d</figure-callout>}}_2,({\mathrm{<figure-callout\; id="2"\; label="d"\; filenames="A20061005339000161.png,A20061005339000171.png"\; state="\{\{state\}\}">d</figure-callout>}}_2=\mathrm{\&tau;}/k);$

Equally, when the user submit to be audio object the time, making uses the same method upgrades modifying factor γ _{(i, j)}, the retrieving of next round is arranged the result who returns according to new similarity.

According to the base vector of asking in the mapping process of subspace, perhaps by relevant feedback mechanism, other media object beyond the training set are accurately navigated in the isomorphism subspace: when the inquiry example of user's submission does not belong to training dataset, the use characteristic extraction procedure extracts example visual feature of image vector V, divides following two kinds of situations to carry out the mapping of new media object to the isomorphism subspace:

(1) if the semantic information of known new media object representation, the subspace base vector that described training obtains according to claim 3 then, method with linear transformation is mapped to the isomorphism subspace that m ties up with vectorial V, with other multimedia object computer general distances in the training set;

(2) if content-based single mode retrieval is adopted in semantic the unknown of new media object representation, return the image similar, user's mark feedback positive example Z={z to inquiring about example ₁..., a _j, stride the medium searching system and calculate coordinate Pos (V)=Pos (z of new media object in m dimension isomorphism subspace with weighted average method ₁) β ₁+ ...+Pos (z _j) β _j, (β ₁+ ...+β _j=1).

Beneficial effect of the present invention:

1) this method has broken through content-based multimedia retrieval only at the restriction of single mode, proposes a kind of completely newly stride the medium search method.This method is analyzed the content characteristic of two kinds of different modalities simultaneously, excavates the canonical correlation on statistical significance between the feature;

2) the subspace mapping method has not only solved the isomerism problem between different modalities, and farthest in the subspace, kept correlation information between the multi-modal feature, this correlation information is actually a kind of semantic association information, so this method has merged semanteme when realizing the feature dimensionality reduction;

3) media object of different modalities can be with the vector representation of isomorphism, and the similarity under polar coordinate system between the compute vector is between the promptly identical mode and the distance between the different modalities.

Description of drawings

Fig. 1 is based on the system framework figure that strides the medium search method of content relevance;

Fig. 2 (a) is the multi-medium data collection distribution schematic diagram before relevant feedback in the isomorphism of the present invention subspace;

Fig. 2 (b) is the multi-medium data collection distribution schematic diagram after relevant feedback in the isomorphism of the present invention subspace;

Fig. 3 (a) is that the present invention serves as that the retrieval example adopts the isomorphism subspace method to obtain result for retrieval with " automobile " image;

Fig. 3 (b) is that the present invention serves as the result for retrieval that the retrieval example directly adopts content characteristic to obtain with " automobile " image;

Fig. 4 (a) is that the present invention serves as the result for retrieval that the retrieval example adopts the isomorphism subspace method to obtain with " war " image;

Fig. 4 (b) is that the present invention serves as the result for retrieval that the retrieval example directly adopts content characteristic to obtain with " war " image.

Embodiment

The bottom content characteristic of different modalities media object, as the aural signature (temporal signatures, frequency domain character, time-frequency characteristics etc.) of visual feature of image (color, texture, shape etc.) with audio frequency, intrinsic dimensionality isomery not only, and express different attributes, can't directly measure similarity.The present invention can analyze the visual signature and the aural signature of isomery simultaneously, and be foundation with the canonical correlation between the feature, carry out the subspace mapping, solved the isomerism and the noncomparabilities problem of striding in the medium retrieval, and the subspace mapping process has farthest kept the correlation information between the initial characteristics.The technical scheme and the step of striding the concrete enforcement of medium search method of content-based correlativity of the present invention are as follows:

1. training data choosing and marking

Canonical correlation inquiry learning between visual signature and the aural signature is to be based upon on the basis of semantic relation, with the method for statistical study, excavates connecting each other on the semantic hierarchies from low-level image feature.Choosing of training data need have view data and voice data to express similar semanteme simultaneously.For example,, choose the picture of expression " dog " resemblance, and the audio-frequency fragments of expression " dog " cry is as training data for " dog " this semantic classes.

In known semantic classes number, under the semantic tagger condition of unknown of view data and voice data, adopt the study of semi-supervised formula, images all in the database and voice data are marked in conjunction with the method for the average cluster of K, and cluster is to different semantic classess, and concrete steps are as follows:

Input: not Biao Zhu image data set Ω and audio data set Γ, semantic classes number Z;

Output: the semantic classes numbering under each view data and each voice data;

Step 1: for semantic classes Z _i, 5 image examples A of random labelling _i, calculate A _iCluster barycenter ICtr _i

Step 2: with ICtr _iBe the initial input of the average clustering algorithm of K, Ω carries out cluster to the whole image data collection, is endowed identical semantic classes numbering in the image examples of identical cluster areas;

Step 3: also adopt step 1 and step 2 to carry out the mark of training data to voice data Γ.

2. the extraction of vision and aural signature

For the view data in each semantic classes, extract the bottom visual signature, comprising: hsv color histogram, color convergence vector CCV and Tamura direction degree are the characteristics of image vector x of every width of cloth image configuration p dimension _p, the image data set composing images eigenmatrix X in the whole semantic classes _{(n * p)}For the voice data in each semantic classes, extract the bottom aural signature, comprise: barycenter (Centroid), decay are the audio feature vector y of each section audio example structure q dimension by these four Mpeg compression domain features of frequency (Rolloff), frequency spectrum flow (Spectral Flux) and root mean square (RMS) _q, the audio data set in the whole semantic classes constitutes audio frequency characteristics matrix Y _{(n * q)}If the duration difference of voice data, the dimension of the audio frequency characteristics vector of extraction are also different, the present invention uses fuzzy clustering method, extracts the cluster barycenter of similar number as audio index in the original audio feature.

3. hold the isomorphism subspace mapping of multi-semantic meaning different modalities media data

On the basis of canonical correlation analysis, study obtains the low n-dimensional subspace n of an optimum, has farthest kept original feature vector X _{(n * p)}And Y _{(n * q)}Between correlativity, algorithm steps is as follows:

Input: image characteristic matrix X _{(n * p)}, audio frequency characteristics matrix Y _{(n * q)}

Output: all images data and the voice data vector representation L in low n-dimensional subspace n _{(n * m)}And M _{(n * m)}

Step 1:, view data all in the database and voice data are divided into different semantic classess with the average cluster of K-by the mode of semi-supervised learning;

Step 2: at v (L)=L ^TL=A ^TX ^TXA=1; V (M)=M ^TM=B ^TY ^TUnder the constraint of YB=1, (L, M) optimization adopt method of Lagrange multipliers to obtain the equation C that form is Ax=λ Bx to make related coefficient ρ=r _XyC _Yy ^-1C _YxA=λ ²C _XxA, the characteristic root of asking for this equation promptly obtains separating of matrix A and B;

Step 3: linear method structure isomorphism subspace promptly becomes m dimension coordinate L with B with characteristics of image vector sum audio frequency characteristics DUAL PROBLEMS OF VECTOR MAPPING with base vector A respectively _{(n * m)}And M _{(n * m)}

4. adopt general distance function to calculate similarity

After the proper vector of all images and voice data converts the m dimensional vector that hangs down in the n-dimensional subspace n to, a large amount of plural numbers appear, in order to calculate the similarity between various mode media datas, the proper vector behind the employing polar form expression dimensionality reduction: x _i'=(x _I1' ..., x _Ik' ..., x _Im'), (x _Ik'=a+bi, (a, b ∈ R)).Therefore, between image and the image, between audio frequency and the audio frequency and the similarity between image and the voice data be calculated as follows with general distance function:

${\mathrm{CCAdis}}_{({\mathrm{xi}}^{\&prime;},{\mathrm{xj}}^{\&prime;})}=\mathrm{sqrt}\underset{k=1}{\overset{m}{\mathrm{\&Sigma;}}}({\left|{x_{\mathrm{ik}}}^{\&prime;}\right|}^2+{\left|{x_{\mathrm{jk}}}^{\&prime;}\right|}^2-2\&times;|{x_{\mathrm{ik}}}^{\&prime;}|\&times;|{x_{\mathrm{jk}}}^{\&prime;}|\&times;\mathrm{Cos}{\mathrm{\&theta;}}_k);$

${\mathrm{\&beta;}}_{\mathrm{ik}}=\mathrm{arctg}(b/a),{\mathrm{\&theta;}}_k=|{\mathrm{\&beta;}}_{\mathrm{ik}}-{\mathrm{\&beta;}}_{\mathrm{jk}}|,|x_{\mathrm{ik}}\text{'}|=\sqrt{a^2+{\mathrm{<figure-callout\; id="2"\; label="b"\; filenames="A20061005339000161.png,A20061005339000171.png"\; state="\{\{state\}\}">b</figure-callout>}}^2},k\&Element;[1,m]$

The user provides inquiry example image by man-machine interface in the retrieving, if this example is in tranining database, then find the m dimension coordinate of inquiry example in the subspace according to the subspace mapping result, with the distance between general distance function calculating and other audio frequency and view data, k image and k the audio frequency nearest with the query image example return to the user as Query Result; Equally, if the inquiry example is a section audio, then retrieve similar audio frequency and image object according to above-mentioned steps.

The present invention supports the retrieval of single mode and strides the retrieval of medium, promptly the user submit a kind of mode to media object as inquiry, in result for retrieval, can comprise the media object of other mode, and can cause new inquiry based on another kind of mode object.

5. relevant feedback

By content-based method, the canonical correlation between study visual signature and the aural signature, thus at utmost keeping realizing the subspace mapping under the constant situation of correlativity, solve feature isomerism problem.But because the wide gap between bottom content and the high-level semantic makes learning outcome and true semanteme there are differences.By user's relevant feedback, mark positive example and negative example in returning Query Result mark middle school idiom justice information from the user, and revise the topological structure of multi-medium data collection in the subspace that study obtains.

If Ω presentation video training set, A represents the audio frequency training set, definition " modifying factor " γ _{(i, j})=Pos (a _i, b _j) (a _i∈ Ω, b _j∈ A), be used to revise similarity between the different modalities media object: Crodis _{(i, j)}=CCAdis _{(i, j)}+ γ _{(i, j)}, modifying factor is initialized as zero; When the user submits image querying example R to, use CCAdis (i, j) the k neighbour image collection C of calculating R in the subspace ₁, use Crodis (i, j)) to calculate the k neighbour audio set C of R in the subspace ₂, the return results of striding the medium retrieval is C ₁And C ₂In user interaction process, the user marks positive example P and negative routine N,  p by relevant feedback in Query Result _i∈ P, order $\mathrm{\&gamma;}(R,p_i)=-\mathrm{\&tau;},(\mathrm{\&tau;}>0),$ And find p according to CCAdis _iK-neighbour T={t in audio database A ₁..., t _j..., t _k, arrange by the ascending order of distance, then in the mode of equal difference, revise the γ value of each element among the set T successively: ${\mathrm{\&gamma;}}_{(R,t_j)}=-\mathrm{\&tau;}+j\&times;d_1,(d_1=\mathrm{\&tau;}/k);$  n _i∈ N, order $\mathrm{\&gamma;}(R,n_i)=\mathrm{\&tau;},(\mathrm{\&tau;}>0),$ And find n according to CCddis _iK-neighbour H={h in audio database A ₁..., h _j..., h _k, arrange by the ascending order of distance, then in the mode of equal difference, revise the γ value of each element among the set H successively: ${\mathrm{\&gamma;}}_{(R,h_j)}=\mathrm{\&tau;}-j\&times;d_2,(d_2=\mathrm{\&tau;}/k);$ Equally, when the user submit to be audio object the time, making uses the same method upgrades modifying factor γ _{(i, j)}The retrieving of next round is arranged the result who returns according to new similarity.

6. the location of new media object

The single multimedia object that the user submits to is defined as the new media object.If the new media object is not in tranining database, also can pass through the subspace base vector, directly navigate in the subspace that training obtains with the method for linearity, perhaps mutual by simple user, accurately navigate in the subspace, remain in the subspace similar semantically simultaneously to multimedia object on every side.At first the use characteristic extraction procedure extracts example visual feature of image vector V, divides following two kinds of situations to carry out the mapping of new media object to the isomorphism subspace:

On the one hand, if the semantic information of known new media object representation, the subspace base vector that obtains according to training then is mapped to the isomorphism subspace of m dimension with the method for linear transformation with vectorial V, with other multimedia object computer general distances in the training set.

On the other hand,, adopt content-based single mode retrieval, return the image similar, user's mark feedback positive example Z={Z to inquiring about example if the new media object representation is semantic unknown ₁... z _j, stride the medium searching system and calculate coordinate Pos (V)=Pos (z of new media object in m dimension isomorphism subspace with weighted average method ₁) β ₁+ ...+Pos (z _j) β _j, (β ₁+ ...+β _j=1).

Embodiment 1

As shown in Figure 2, provided the example of some training datasets topological structures in low-dimensional isomorphism subspace.Describe the concrete steps that this example is implemented in detail below in conjunction with method of the present invention, as follows:

(1) view data and the voice data of 7 semantemes of collection (birds, dog, automobile, war, tiger, squirrel, monkey) are as training dataset;

(2) adopt feature extraction program to extract hsv color histogram, color convergence vector CCV and the Tamura direction degree feature of image, be the visual signature vector of every width of cloth image configuration 500 dimensions, be respectively the visual signature matrix of 7 semantic classes structure 70 * 500 dimensions;

(3) adopt feature extraction program to extract the barycenter (Centroid) of audio frequency, decay by these four Mpeg compression domain features of frequency (Rolloff), frequency spectrum flow (Spectral Flux) and root mean square (RMS);

(4) the duration difference of audio example, the proper vector length that extracts is also different, adopt fuzzy clustering method, the audio frequency characteristics vector unified specification of different dimensions is changed into the vector of 40 dimensions, as the index of every section audio example, be respectively the aural signature matrix of 7 semantic classes structure 70 * 40 dimensions;

(5) under the Matlab7.0 environment, use the canonical correlation analysis function, learn the pairing vision of training data of 7 semantic classess and the correlativity between the aural signature matrix respectively.And carrying out subspace mapping with linear method, the eigenmatrix with 70 * 500 and 70 * 40 is transformed into 70 * 40 and 70 * 40 new feature matrix respectively;

(6) basis ${\mathrm{CCAdis}}_{({x_i}^{\&prime;},{x_j}^{\&prime;})}=\mathrm{sqrt}\underset{k=1}{\overset{m}{\mathrm{\&Sigma;}}}({\left|{x_{\mathrm{ik}}}^{\&prime;}\right|}^2+{\left|{x_{\mathrm{jk}}}^{\&prime;}\right|}^2-2\&times;|{x_{\mathrm{ik}}}^{\&prime;}|\&times;|{x_{\mathrm{jk}}}^{\&prime;}|\&times;{\mathrm{Cos\&theta;}}_k)$ Calculate the distance between the 40 characteristics of image vector sum audio frequency characteristics vectors of tieing up in the subspace, return and inquire about example nearest 20 width of cloth images and 20 section audios:

(7) in striding the medium retrieving, the user can be undertaken alternately by man-machine interface, mark striding the medium result for retrieval, system learns feedback positive example and the negative example of feedback that the user submits to automatically, the semantic information of extracting is used for revising the topological structure of multi-medium data collection in the isomorphism subspace, promptly uses respectively ${\mathrm{\&gamma;}}_{(R,t_j)}=-\mathrm{\&tau;}+j\&times;{\mathrm{<figure-callout\; id="1"\; label="d"\; filenames="A20061005339000161.png,A20061005339000171.png"\; state="\{\{state\}\}">d</figure-callout>}}_1,({\mathrm{<figure-callout\; id="1"\; label="d"\; filenames="A20061005339000161.png,A20061005339000171.png"\; state="\{\{state\}\}">d</figure-callout>}}_1=\mathrm{\&tau;}/k)$ With ${\mathrm{\&gamma;}}_{(R,h_j)}=\mathrm{\&tau;}-j\&times;{\mathrm{<figure-callout\; id="2"\; label="d"\; filenames="A20061005339000161.png,A20061005339000171.png"\; state="\{\{state\}\}">d</figure-callout>}}_2,({\mathrm{<figure-callout\; id="2"\; label="d"\; filenames="A20061005339000161.png,A20061005339000171.png"\; state="\{\{state\}\}">d</figure-callout>}}_2=\mathrm{\&tau;}/k)$ Revise around the positive example and the topological structure of multimedia object around the negative example.

Fig. 2 is an example with squirrel, birds and automobile, has shown in the isomorphism subspace that dimensionality reduction mapping obtains, and uses the theoretical distribution of the data of media object collection that CCAdis measures out, and through relevant feedback repair apart from after, the corresponding distribution situation that adopts Crodis to measure out.In Fig. 2 (a), and the image data set of CCAdis minimum is the image of birds between the squirrel audio data set, through relevant feedback, Crodis distance between squirrel audio frequency and the squirrel image " has furthered ", " pushed away " the Crodis distance between squirrel audio frequency and the birds image far away, and the topological relation of the topological relation of squirrel image inside and squirrel audio frequency inside remains unchanged substantially, shown in Fig. 2 (b).

Can see,, can learn the correlativity between image and voice data preferably, solve the isomerism problem between the different modalities media data, effectively realize striding the distance metric of medium by method of the present invention; And by relevant feedback, learnt the semantic information in the user interaction process, the distribution of multimedia number pick collection in the subspace meets the relation between the high-level semantic more.

Embodiment 2

As shown in Figure 4, provided one " war " promptly semantic retrieval example.Describe the concrete steps that this example is implemented in detail below in conjunction with method of the present invention, as follows:

(1) input be the semantic colour picture of a width of cloth " war " as the inquiry example, system finds the vector representation in the isomorphism subspace of this width of cloth picture correspondence;

(2) the subspace vector that adopts existing conversion method of data format will inquire about the example correspondence shows with polar mode;

(3) calculate the distance between other images and audio frequency in this inquiry example and database with general distance function, return preceding 10 nearest images and preceding 10 nearest audio example;

(4) directly use the bottom content characteristic of inquiring about example in addition, do not shine upon and do not carry out the subspace, mate with the content characteristic of other images in the database, promptly use content-based single mode search method, return preceding 10 images the most similar, the result for retrieval that obtains with the method for describing among the present invention compares.

The operation result of this example shows in accompanying drawing 4, wherein inquiring about example is the semantic colour blast picture of a reflection " war ", method with the present invention's description, shown in figure (a), (b) in contrast directly uses the bottom visual signature to mate the similar image that returns in the result of mating in the isomorphism subspace and returning.Even use coloured image, also can in preceding 10 result for retrieval, return and retrieve example and express common semantic black and white picture as the retrieval example.

Can see that method of the present invention can be understood the common semanteme of coloured image and black white image well, realize the mutual retrieval of black white image and coloured image, efficiently solve the accurate tolerance of multi-medium data on similarity that differs greatly on the content characteristic; And adopt content-based single mode search method, can only return and inquire about example similar picture on visual signature.

Claims (6)

1. A content-based correlativity stride the medium search method, it is characterized in that may further comprise the steps:

   (1) gathers the object of different modalities from multimedia database, i.e. image and voice data;

   (2) visual signature of extraction view data, and the aural signature of voice data, vision that the extraction of employing canonical correlation analysis obtains and the canonical correlation between the aural signature;

   (3) adopt isomorphism subspace mapping algorithm, the visual feature vector of view data and the aural signature vector of voice data are mapped in the isomorphism subspace of a low-dimensional simultaneously, realize the unified expression of different modalities media data;

   (4) adopt polar mode to define general distance function, the correlativity size between tolerance different modalities media data, and stride the medium retrieval on this basis;

   (5), be used for extracting the priori of user interactions, to revise the topological structure of multi-medium data collection in the isomorphism subspace based on the relevant feedback mechanism of incremental learning;

   (6),, other media object beyond the training set are accurately navigated in the isomorphism subspace perhaps by relevant feedback mechanism according to the base vector of asking in the mapping process of subspace.
2. 2, content-based correlativity according to claim 1 strides the medium search method, it is characterized in that, the visual signature of described extraction view data, and the aural signature of voice data, vision that the extraction of employing canonical correlation analysis obtains and the canonical correlation between the aural signature: the level image visual signature constitutes the characteristics of image vector of p dimension, the bottom aural signature of audio frequency constitutes q dimension audio feature vector, adopts canonical correlation analysis to learn visual feature of image X simultaneously _{(n * p)}Aural signature Y with audio frequency _{(n * q)}, the eigenmatrix X of isomery _{(n * p)}And Y _{(n * q)}Between related coefficient be calculated as follows:

   $\mathrm{\&rho;}=r(L,M)=\frac{A^T{C}_{\mathrm{xy}}B}{\sqrt{A^T{C}_{\mathrm{xx}}A{B}^T{C}_{\mathrm{yy}}B}},(C_{\mathrm{xy}}=\left[\begin{array}{cc}{C}_{\mathrm{xx}}& C_{\mathrm{xy}}\\ C_{\mathrm{yx}}& C_{\mathrm{yy}}\end{array}\right]=C)---1$

   

   Wherein A and B are linear transformation, by formula 2 turn to relevant between less union variable L and M having relevant between the eigenmatrix X of more a plurality of variablees and the Y, the numeric distribution of A and B is determined the space correlation distribution form of X and Y, the numerical values recited of A and B determine to the significance level of dependent variable.
3. 3, content-based correlativity according to claim 1 strides the medium search method, it is characterized in that, described employing isomorphism subspace mapping algorithm, the visual feature vector of view data and the aural signature vector of voice data are mapped in the isomorphism subspace of a low-dimensional simultaneously, realize the unified expression of different modalities media data: isomorphism subspace mapping algorithm is on the basis of canonical correlation analysis, study obtains the low n-dimensional subspace n of an optimum, has farthest kept original feature vector X _{(n * p)}And Y _{(n * q)}Between correlativity, algorithm steps is as follows:

   Input: image characteristic matrix X _{(n * p)}, audio frequency characteristics matrix Y _{(n * q)}

   Output: all images data and the voice data vector representation L in low n-dimensional subspace n _{(n * m)}And M _{(n * m)}

   Step 1:, view data all in the database and voice data are divided into different semantic classess with the average cluster of K by the mode of semi-supervised learning;

   Step 2: under the constraint of formula 3, make related coefficient ρ=r (L, M) optimization,

   v(L)＝L ^TL＝A ^TX ^TXA＝1；v(M)＝M ^TM＝B ^TY ^TYB＝1 3

   Adopt method of Lagrange multipliers to obtain the equation C that form is Ax=λ Bx _XyC _Yy ^-1C _YxA=λ ²C _XxA, the characteristic root of asking for this equation promptly obtains separating of matrix A and B;

   Step 3: linear method structure isomorphism subspace promptly becomes m dimension coordinate L with B with characteristics of image vector sum audio frequency characteristics DUAL PROBLEMS OF VECTOR MAPPING with base vector A respectively _{(n * m)}And M _{(n * m)}
4. 4, this content-based correlativity according to claim 1 strides the medium search method, it is characterized in that, the polar mode of described employing defines general distance function, correlativity size between tolerance different modalities media data, and stride the medium retrieval on this basis: image and voice data in the m n-dimensional subspace n with polar formal definition proper vector x _i'=(x _Il' ..., x _Ik' ..., x _Im'), (x _Ik'=a+bi, (a, b ∈ R)), between image and the image, between audio frequency and the audio frequency and the similarity between image and the voice data be calculated as follows with general distance function:

   $\mathrm{CCAdi}{s}_{(x{i}^{\&prime;},x{j}^{\&prime;})}=\mathrm{sqrt}\underset{k=1}{\overset{m}{\mathrm{\&Sigma;}}}({\left|{x_{\mathrm{ik}}}^{\&prime;}\right|}^2+{\left|{x_{\mathrm{jk}}}^{\&prime;}\right|}^2-2\&times;|{x_{\mathrm{ik}}}^{\&prime;}|\&times;|{x_{\mathrm{jk}}}^{\&prime;}|\&times;\mathrm{Cos}{\mathrm{\&theta;}}_k);---44$

   ${\mathrm{\&beta;}}_{\mathrm{ik}}=\mathrm{arctg}(b/a),{\mathrm{\&theta;}}_k=|{\mathrm{\&beta;}}_{\mathrm{ik}}-{\mathrm{\&beta;}}_{\mathrm{jk}}|,=\left|{x_{\mathrm{ik}}}^{\&prime;}\right|=\sqrt{a^2+b^2},k\&Element;[1,m]$

   The user provides inquiry example image by man-machine interface in the retrieving, if this example is in tranining database, then find the m dimension coordinate of inquiry example in the subspace according to the subspace mapping result, with the distance between general distance function calculating and other audio frequency and view data, k image and k the audio frequency nearest with the query image example return to the user as Query Result; Equally, if the inquiry example is a section audio, then retrieve similar audio frequency and image object according to above-mentioned steps.
5. 5, this content-based correlativity according to claim 1 strides the medium search method, it is characterized in that, described relevant feedback mechanism based on incremental learning, be used for extracting the priori of user interactions, to revise the topological structure of multi-medium data collection in the isomorphism subspace: system can commonly use the perception priori that the family provides in relevant feedback process middle school, if Ω presentation video training set, A represents the audio frequency training set, definition " modifying factor " γ _{(i, j)}=Pos (a _i, b _j) (a _i∈ Ω, b _j∈ A), be used to revise similarity between the different modalities media object: Crodis _{(i, j)}=CCAdis _{(i, j)}+ γ _{(i, j)}, modifying factor is initialized as zero;

   When the user submits image querying example R to, use CCAdis _{(i, j)}Calculate the k neighbour image collection C of R in the subspace ₁, use Crodis _{(i, j)}Calculate the k neighbour audio set C of R in the subspace ₂, the return results of striding the medium retrieval is C ₁And C ₂

   In user interaction process, the user marks positive example P and negative routine N,  by relevant feedback in Query Result _Pi∈ P makes γ (R, p _iThe τ of)=-, (τ＞0), and find p according to CCAdis _iK-neighbour T={t in audio database A ₁..., t _j..., t _k, arrange by the ascending order of distance, then in the mode of equal difference, revise the γ value of each element among the set T successively: γ (R, t _jτ+the j of)=-* d ₁, (d ₁=τ/k);  _Ni∈ N, make γ (R, ni)=τ, (τ＞0), and find the k-neighbour H={h of ni in audio database A according to CCAdis ₁... h _j... h _k, arrange by the ascending order of distance, then in the mode of equal difference, revise the γ value of each element among the set H successively:

   γ(R，h _j)＝τ-j×d ₂，(d ₂＝τ/k)；

   Equally, when the user submit to be audio object the time, making uses the same method upgrades modifying factor γ (i, j), the retrieving of next round is arranged the result who returns according to new similarity.
6. 6. this content-based correlativity according to claim 1 strides the medium search method, it is characterized in that, described according to the base vector of asking in the mapping process of subspace, perhaps by relevant feedback mechanism, other media object beyond the training set are accurately navigated in the isomorphism subspace: when the inquiry example of user's submission does not belong to training dataset, the use characteristic extraction procedure extracts example visual feature of image vector V, divides following two kinds of situations to carry out the mapping of new media object to the isomorphism subspace:

   (1) if the semantic information of known new media object representation, the subspace base vector that described training obtains according to claim 3 then, method with linear transformation is mapped to the isomorphism subspace that m ties up with vectorial V, with other multimedia object computer general distances in the training set;

   (2) if content-based single mode retrieval is adopted in semantic the unknown of new media object representation, return the image similar, user's mark feedback positive example Z={z to inquiring about example ₁..., z _j, stride the medium searching system and calculate coordinate Pos (V)=Pos (z of new media object in m dimension isomorphism subspace with weighted average method ₁) β ₁+ ...+Pos (z _j) β _j, (β ₁+ ...+β _j=1).

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