CN104679902A - Information abstract extraction method in conjunction with cross-media fuse - Google Patents
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Abstract
The invention provides an information abstract extraction method in conjunction with cross-media fuse. The method comprises the steps of classifying inputted multimedia data (characters, images, audios, videos, etc.) according to a data type; carrying out the dimension formatting for the isomerous multimedia data, establishing a text tag of the data, and acquiring a dimensional image and the text tag; clustering the dimensional image data, and carrying out the association detection of the text tag; fusing a plurality of dimensional images into one image in different types; and finally generating a cross-media information abstract. By adopting the method, the fuse image of the information of each type can be checked by virtue of the information abstract, and the corresponding multimedia data can be rapidly accessed.
Description
Technical field
The present invention relates to the informative abstract extracting method of a kind of combination across Media Convergence, belong to information extraction field.
Background technology
We live in an information age, and magnanimity information increases, and internet every day is in the information of new increase, and the storage mode of information is day by day diversified, and text, image, audio frequency, video are the basic existence forms of multimedia resource.Nowadays polytype media data mixes and deposits, media data organization complex structure, but dissimilar media data never ipsilateral expresses same semanteme, need in information extraction, according to the various contacts existed between media, to cross another kind of media from a kind of media.Therefore, how to cross over the boundary between media, how to extract the potential relevance between media, become current information extraction institute facing challenges.
Media form is mixed and the large data of depositing, existing method is mainly realized by the feature identification of same media, be difficult to cross over the semantic gap between multimedia, the similarity that such as, intrinsic dimensionality between the visual signature of image and the aural signature of audio frequency is different and cannot directly measure between them, therefore, existing information extracting method can not very well for user provides thumbnail directly perceived (or informative abstract), how by a large amount of multimedia data classification of mixing and extraction, become one of information extraction gordian technique difficult problem needing solution badly, also be the heat subject studied at present.
The method such as existing ripe Text Mining Technology, image characteristics extraction algorithm, audio scene identification, speech recognition, video scene segmentation, key-frame extraction can extract the semantic information of single medium, how these algorithms are combined, by the feature information extraction of different dimension, the multimedia information extracting system of formation processing, we solve this problem by these middle-dimentional media of image.
Summary of the invention
For the problems referred to above, the present invention proposes the informative abstract extracting method of a kind of combination across Media Convergence.By adopting the method different dimension data being turned to image with dimension, solve the problem being difficult to cross over semantic information of multimedia wide gap.By image clustering method, thus indirectly by multimedia data classification and extraction, generate and make a summary across media information.
The present invention proposes the informative abstract extracting method of a kind of combination across Media Convergence.First the multi-medium data (word, image, audio frequency, video etc.) of input is classified by data type; Again different dimension multi-medium data is set up the text label of data with dimensionization, obtain dimension image and text label together; Then by same dimensional data image cluster and carry out text label relevance inspection; Several same dimension images of sub-category fusion are a sub-picture again; Finally generate and make a summary across media information.User can check the fused images of every category information by informative abstract, and can multi-medium data corresponding to fast access.
The present invention proposes the informative abstract extracting method of a kind of combination across Media Convergence, comprises the following steps:
Step one: be urtext data by data type classifications by (word, image, audio frequency, video) in the multi-medium data of input
, raw image data
, original audio data
, original video data
;
Step 2: arrange view data dimension (image pixel) standard value, sets up the same Wei Tuxiangyangbenku with text label, carries out different dimension multi-medium data with dimensionization process, adopts corresponding disposal route according to the difference of data type;
Step 3: to processed same dimensional data image, the accuracy definite threshold required for cluster
, carry out cluster according to image clustering algorithm, carry out the inspection of text label relevance according to the text label of every class data, by the data cluster again do not satisfied condition, until the data bulk do not satisfied condition is less than threshold value
, can obtain
roughly the same dimensional data image
address, i.e. index
;
Step 4: to the same dimensional data image of cluster, according to a kind of fusion rule, merge, thus the fused images obtaining each roughly the same dimensional data image
;
Step 5: according to each the roughly the same fused images of dimensional data image and index, information generated is made a summary.
Compared with the conventional method, advantage of the present invention is:
1, express semantic for the multi-medium data of different dimension with same dimensional data image, span the boundary between media, and use the related algorithm process multi-medium data of image procossing;
2, image clustering method is checked with text label relevance and is combined, and ensure that the High relevancy between the accuracy of classification and data.
Accompanying drawing explanation
Fig. 1 is process flow diagram of the present invention;
Fig. 2 is the same dimensionization method flow diagram of different dimension data in the present invention;
Fig. 3 checks schematic diagram with dimensional data image cluster and text label relevance in the present invention.
specific implementation method
Below in conjunction with the drawings and specific embodiments, the present invention is described in further detail:
Step one: be urtext data by data type classifications by (word, image, audio frequency, video) in the multi-medium data of input
, raw image data
, original audio data
, original video data
.
Step 2: see Fig. 2, arranges view data dimension (image pixel) standard value, sets up the same Wei Tuxiangyangbenku with text label, carries out different dimension multi-medium data with dimensionization process, the disposal route corresponding according to the different employing of data type;
Existing classification results is
group urtext data,
group raw image data,
group original audio data,
group original video data, will
group urtext data
be treated to same dimensional data image
, will
group raw image data
be treated to same dimensional data image
, will
group original audio data
be treated to same dimensional data image
, will
group original video data
be treated to same dimensional data image
, detailed step is as follows;
1) by urtext data
be treated to same dimensional data image
process and associative operation;
A) pre-service, utilizes certain Text Mining Technology (text mining as based on semantic understanding), by urtext data
in often organize text message paragraph keyword extraction be label
;
B) will
group text data corresponds to same dimensional data image according to label keyword and Sample Storehouse
, wherein, one group of text may correspond to multiple label and same dimensional data image,
corresponding sample image can be expressed as
.
2) by raw image data
be treated to same dimensional data image
process and associative operation;
A) pre-service raw image data
, utilizing related algorithm to strengthen key feature (as rejected background area), obtaining the image after processing
;
B) for image
, utilize certain image scaling techniques (as bicubic interpolation and small echo reversed interpolation) to be scaled same dimensional data image
(with Sample Storehouse with tieing up);
C) by same dimensional data image
adopt certain recognition methods (the image characteristics extraction algorithm as view-based access control model information) and Sample Storehouse comparison, obtain the text label of image, result is deposited in
.
3) by original audio data
be treated to same dimensional data image
process and associative operation;
A) pre-service original audio data
, utilize related algorithm to extract audio scene (audio scene recognition method as based on probability latent semantic analysis), the key features such as language semantic (speech recognition as based on neural network), obtain the text label extracted
;
B) for the text label extracted
, text label is corresponding with Sample Storehouse, obtains same dimensional data image
, wherein, may correspond to multiple label and same dimensional data image with group audio frequency,
corresponding multiple sample images can be expressed as
.
4) by original video data
be treated to same dimensional data image
process and associative operation;
A) pre-service original video data
, utilize a certain Algorithm of Scene (the video scene partitioning algorithm as based on semanteme), for each video
, after obtaining split sence
individual video segment
;
B) for each video segment
, adopt a certain extraction method of key frame (the multiple features fusion key-frame extraction as based on clustering algorithm), obtain key frame images
, the set of the key frame images of each video is designated as
;
C) for key frame images
, utilize related algorithm to strengthen key feature (as rejected background area);
D) same dimensional data image is scaled to certain image scaling techniques of processed imagery exploitation (as bicubic interpolation and small echo reversed interpolation)
(with Sample Storehouse with tieing up);
E) by same dimensional data image
, adopt certain recognition methods and Sample Storehouse comparison, obtain the text label of image, result is deposited in
.
Step 3: see Fig. 3, to processed same dimensional data image, the accuracy definite threshold required for cluster
cluster (image clustering as based on genetic algorithm) is carried out according to certain image clustering algorithm, the inspection of text label relevance is carried out, by the data cluster again do not satisfied condition, until the data bulk do not satisfied condition is less than threshold value according to the text label of every class data
, can index be obtained
, for
roughly the same dimensional data image
address, detailed step is as follows:
1) to processed same dimensional data image, the accuracy definite threshold required for cluster
,
less, classification quantity is more, classifies more accurate, otherwise classification quantity is fewer;
2) cluster (image clustering as based on genetic algorithm) is carried out according to certain image clustering algorithm, store the same dimension image address of cluster, for the same class of cluster with tieing up image, extract the text label of its correspondence, the text label relevance of carrying out text label and image clustering result is checked;
3) for the same dimensional data image of cluster, if the quantity not meeting text label relevance test condition is greater than threshold value
, then the data do not satisfied condition are rejected this class, again become the same dimensional data image of non-cluster, and according to identical or different clustering method cluster again, until the data bulk do not satisfied condition is less than threshold value
;
4) classification results is stored with the form of address, obtain index
, for
roughly the same dimensional data image
address.
Step 4: to the same dimensional data image of cluster, according to a certain fusion rule (as choosing the more piece image of target), merge, thus the fused images obtaining each roughly the same dimensional data image
;
Press index successively
take out
roughly the same dimensional data image
, according to a certain fusion rule, merge, thus obtain the fused images of each roughly the same dimensional data image
.
Step 5: according to each the roughly the same fused images of dimensional data image and index, information generated is made a summary;
By the fused images obtained
and index
information generated is made a summary
, user can check fused images, the multi-medium data that access is corresponding.
Claims (8)
1. combine the informative abstract extracting method across Media Convergence, it is characterized in that, first the multi-medium data (word, image, audio frequency, video etc.) of input is classified by data type; Again different dimension multi-medium data is set up the text label of data with dimensionization, obtain dimension image and text label together; Then by same dimensional data image cluster and carry out text label relevance inspection; Several same dimension images of sub-category fusion are a sub-picture again; Finally generate and make a summary across media information; Described method at least comprises the following steps:
Step one: be urtext data by data type classifications by (word, image, audio frequency, video) in the multi-medium data of input
, raw image data
, original audio data
, original video data
;
Step 2: arrange view data dimension (image pixel) standard value, sets up the same Wei Tuxiangyangbenku with text label, carries out different dimension multi-medium data with dimensionization process, adopts corresponding disposal route according to the difference of data type;
Step 3: to processed same dimensional data image, the accuracy definite threshold required for cluster
, carry out cluster according to image clustering algorithm, carry out the inspection of text label relevance according to the text label of every class data, by the data cluster again do not satisfied condition, until the data bulk do not satisfied condition is less than threshold value
, can obtain
roughly the same dimensional data image
address, i.e. index
;
Step 4: to the same dimensional data image of cluster, according to a kind of fusion rule, merge, thus the fused images obtaining each roughly the same dimensional data image
;
Step 5: according to each the roughly the same fused images of dimensional data image and index, information generated is made a summary.
2. a kind of combination according to claim 1 is across the informative abstract extracting method of Media Convergence, to it is characterized in that in step 2 and by urtext data
be treated to same dimensional data image
process and associative operation, at least further comprising the steps of:
1) pre-service, utilizes Text Mining Technology, by urtext data
in often organize text message paragraph keyword extraction be label
;
2) will
group text data corresponds to same dimensional data image according to label keyword and Sample Storehouse
, wherein, one group of text may correspond to multiple label and same dimensional data image,
corresponding sample image can be expressed as
.
3. a kind of combination according to claim 1 is across the informative abstract extracting method of Media Convergence, to it is characterized in that in step 2 and by raw image data
be treated to same dimensional data image
process and associative operation, at least further comprising the steps of:
1) pre-service raw image data
, utilize related algorithm to strengthen key feature, obtain the image after processing
;
2) for image
, utilize image scaling techniques to be scaled same dimensional data image
(with Sample Storehouse with tieing up);
3) by same dimensional data image
adopt image-recognizing method and Sample Storehouse comparison, obtain the text label of image, result is deposited in
.
4. a kind of combination according to claim 1 is across the informative abstract extracting method of Media Convergence, it is characterized in that original audio data in step 2
be treated to same dimensional data image
process and associative operation, at least further comprising the steps of:
1) pre-service original audio data
, utilize related algorithm to extract audio scene, the key features such as language semantic, obtain the text label extracted
;
2) for the text label extracted
, text label is corresponding with Sample Storehouse, obtains same dimensional data image
, wherein, may correspond to multiple label and same dimensional data image with group audio frequency,
corresponding multiple sample images can be expressed as
.
5. a kind of combination according to claim 1 is across the informative abstract extracting method of Media Convergence, it is characterized in that original video data in step 2
be treated to same dimensional data image
process and associative operation, at least further comprising the steps of:
1) pre-service original video data
, utilize Algorithm of Scene, for each video
, after obtaining split sence
individual video segment
;
2) for each video segment
, adopt extraction method of key frame, obtain key frame images
, the set of the key frame images of each video is designated as
;
3) for key frame images
, utilize related algorithm to strengthen key feature;
4) same dimensional data image is scaled to processed imagery exploitation image scaling techniques
(with Sample Storehouse with tieing up);
5) by same dimensional data image
, adopt image-recognizing method and Sample Storehouse comparison, obtain the text label of image, result is deposited in
.
6. a kind of combination according to claim 1 is across the informative abstract extracting method of Media Convergence, it is characterized in that to same dimension image clustering and the process setting up index in step 3, at least further comprising the steps of:
1) to processed same dimensional data image, the accuracy definite threshold required for cluster
,
less, classification quantity is more, classifies more accurate, otherwise classification quantity is fewer;
2) carry out cluster according to image clustering algorithm, store the same dimension image address of cluster, for the same class of cluster with tieing up image, extract the text label of its correspondence, the relevance of carrying out text label and image clustering result is checked;
3) for the same dimensional data image of cluster, if the quantity not meeting test condition is greater than threshold value
, then the data do not satisfied condition are rejected this class, again become the same dimensional data image of non-cluster, and according to identical or different method cluster again, until the data bulk do not satisfied condition is less than threshold value
;
4) classification results is stored with the form of address, obtain index
, for
roughly the same dimensional data image
address.
7. a kind of combination according to claim 1 is across the informative abstract extracting method of Media Convergence, it is characterized in that in step 4, sub-category fusion is the process of a width with dimensional data image, at least further comprising the steps of:
1) index is pressed successively
take out
roughly the same dimensional data image
, according to a kind of fusion rule, merge, thus obtain the fused images of each roughly the same dimensional data image
.
8. a kind of combination according to claim 1 is across the informative abstract extracting method of Media Convergence, it is characterized in that according to the fusion of each category information same dimension image and index in step 5, the process of information generated summary, at least further comprising the steps of:
1) fused images will obtained
and index
information generated is made a summary
.
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| CN111488490A (en) * | 2020-03-31 | 2020-08-04 | 北京奇艺世纪科技有限公司 | Video clustering method, device, server and storage medium |
| CN111767395A (en) * | 2020-06-30 | 2020-10-13 | 平安国际智慧城市科技股份有限公司 | Abstract generation method and system based on picture |
| CN111767395B (en) * | 2020-06-30 | 2023-12-26 | 平安国际智慧城市科技股份有限公司 | Abstract generation method and system based on pictures |
| CN112925902A (en) * | 2021-02-22 | 2021-06-08 | 新智认知数据服务有限公司 | Method and system for intelligently extracting text abstract in case text and electronic equipment |
| CN112925902B (en) * | 2021-02-22 | 2024-01-30 | 新智认知数据服务有限公司 | Method, system and electronic equipment for intelligently extracting text abstract from case text |
| CN113505201A (en) * | 2021-07-29 | 2021-10-15 | 宁波薄言信息技术有限公司 | Contract extraction method based on SegaBert pre-training model |
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