CN104142920A - Online image retrieval system - Google Patents
Online image retrieval system Download PDFInfo
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- CN104142920A CN104142920A CN201310161562.XA CN201310161562A CN104142920A CN 104142920 A CN104142920 A CN 104142920A CN 201310161562 A CN201310161562 A CN 201310161562A CN 104142920 A CN104142920 A CN 104142920A
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
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- G06—COMPUTING; CALCULATING OR COUNTING
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Abstract
The invention relates to an online image retrieval system and belongs to the field of a software product. The system is characterized by being used for combining the features of movable picture search based on full study on mobile internet, reinforcement learning, and picture search and mining theory; researching an image feature combinatorial optimization method based on parallel reinforcement learning, combining image feature cluster index parallel construction methods of image physical features and semantic features, and designing a practical online image retrieval system based on data updating of logic reinforcement learning and actively discovering strategy of new data, so as to greatly improve the retrieval efficiency, and gain a great advance in the field of the software product.
Description
Technical field
At a ray image retrieval system, belong to software product field.
Background technology
The E-commerce market season Surveillance > > of < < 2012Q3 China Mobile according to China's authoritative mobile Internet third party data mining and integrated marketing mechanism Chinese mugwort matchmaker's consulting (iiMediaResearch) issue shows, within 2011, China Mobile's ecommerce userbase has reached 0.92 hundred million people, to the end of the year 2012, China Mobile's ecommerce userbase will reach 1.46 hundred million people, increase by 58.7% on a year-on-year basis.Expected for the end of the year 2015, mobile e-business userbase will reach 3.48 hundred million people.Within 2011, China Mobile's E-commerce market scale has reached 156.7 hundred million yuan, and to the end of the year 2012, China Mobile's E-commerce market scale will reach 251.5 hundred million yuan, increase on year-on-year basis 60.5%.Expected for the end of the year 2015, mobile e-business market scale will reach 1046.7 hundred million yuan.Mobile e-business is in fast-developing period.
Summary of the invention
Object of the present invention is achieved through the following technical solutions:
At a ray image retrieval system, belong to software product.Be characterized in: fully study on the basis of mobile Internet, intensified learning, picture search and excavation theory, feature in conjunction with moving images search, the characteristics of image cluster index parallel constructing method of image characteristic combination optimization method, combining image physical features and semantic feature and the Data Update of logic-based intensified learning and the new data active discovery strategy of research based on parallel intensified learning, design one actual in ray image retrieval system.
Further, above-mentioned is a kind of at ray image retrieval system, it is characterized in that: described image indexing system is CBIR, groundwork concentrates in identification and the color of Description Image, texture, shape, spatial relationship, and on the measuring similarity of Image Feature Matching, after image characteristics extraction and method for measuring similarity are determined, retrieval has just become the process of searching the image the most similar to given image in image data base.
Further, above-mentioned is a kind of at ray image retrieval system, it is characterized in that: described image is that image is comprised of by certain semantic association relation (cluster index) all image blocks for the overall situation, similar image has the identical cluster index that quantity does not wait.
Further, above-mentioned is a kind of at ray image retrieval system, it is characterized in that: described cluster is on the basis that image is cut apart and assemblage characteristic extracts, and adopts the k-means clustering algorithm based on Map/Reduce to implement cluster to image block.
Further, above-mentioned is a kind of at ray image retrieval system, it is characterized in that: described cluster is that image block to be clustered is stored on different back end dispersedly in its bottom layer realization, each piece can also copy some parts, be stored on different back end, to reach fault-tolerant object, the back end in cluster is in charge of the storage on its place node.
Further, above-mentioned is a kind of at ray image retrieval system, it is characterized in that: described image retrieval is on the basis of image block cluster, regard image block cluster index as vision keyword, all vision keywords have formed vision keyword dictionary, and image is by the feature clustering vector representation consisting of a series of vision keyword.
Embodiment
At a ray image retrieval system, belong to software product.Be characterized in: fully study on the basis of mobile Internet, intensified learning, picture search and excavation theory, feature in conjunction with moving images search, the characteristics of image cluster index parallel constructing method of image characteristic combination optimization method, combining image physical features and semantic feature and the Data Update of logic-based intensified learning and the new data active discovery strategy of research based on parallel intensified learning, design one actual in ray image retrieval system.
One of this project main research is for cutting apart as data research image for Large Scale Graphs and assemblage characteristic extraction problem.This project is intended starting with from cutting apart of image, based on to the considering of local characteristics of image and combination of multiple features, adopts cutting apart and assemblage characteristic extraction problem of intensified learning method research image.And adopt D-S evidence theory to merge learning outcome.
The core pixel of the target and background in the image that user uses mobile terminal to take pictures to obtain has higher determinacy, and pixel is large for the degree of membership of target; And marginal portion is owing to being subject to the interference of other classification pixels, there is higher uncertainty, in addition, user also often shows as uncertainty to the subjectivity of image understanding and the demand of search, the uncertain transformation model of qualitative, quantitative can be expressed the uncertainty of concept, the uncertainty of reduction Concept Hierarchies well, can effectively study the uncertain problem of image in cutting apart, this project adopts the image segmentation algorithm of the uncertain transformation model of qualitative, quantitative to solving the ignorance of traditional images partitioning algorithm to uncertain information.
The final D-S evidence theory that adopts merges the learning outcome of each agent.In supposing the system, there is n agent, after all agent have completed the study of one-period, their Q table is merged.Q (s in normalization Q table, a) value, utilize D-S evidence to merge, first the learning outcome of agent1 and agent2 is merged, then fusion results and agent3 are merged, the like, carry out altogether (n-1) inferior fusion, each fusion is all two fusions between evidence.
Due to the feature extraction of the view data of magnanimity and exponential Feature Combination, need parallelization and work to raise the efficiency.MapReduce can not only be for the treatment of large-scale data, and possesses the characteristics such as the standby management of automatically parallelizing, load balancing and calamity; And the retractility of MapReduce is very good, be better than most of distributed treatment frameworks in the past, therefore, native system has adopted this distributed treatment framework of MapReduce to learn the Feature Combination of moving images classification.
Early stage investigation according to project team to market, does not also have in conjunction with the company of the moving images search of mobile Internet, intensified learning, picture search and digging technology, does not domesticly have other direct competitions opponent at present, so we have larger advantage.Project team possesses stronger technical foundation at aspects such as mobile Internet, intensified learning, picture search and excavations, can guarantee the technical service that is in time to utilize first-strike advantage, and this project will have good industrialization prospect.
Claims (6)
1. at a ray image retrieval system, belong to software product field.Be characterized in: fully study on the basis of mobile Internet, intensified learning, picture search and excavation theory, feature in conjunction with moving images search, the characteristics of image cluster index parallel constructing method of image characteristic combination optimization method, combining image physical features and semantic feature and the Data Update of logic-based intensified learning and the new data active discovery strategy of research based on parallel intensified learning, design one actual in ray image retrieval system.
2. according to claim 1 a kind of at ray image retrieval system, it is characterized in that: described image indexing system is CBIR, groundwork concentrates in identification and the color of Description Image, texture, shape, spatial relationship, and on the measuring similarity of Image Feature Matching, after image characteristics extraction and method for measuring similarity are determined, retrieval has just become the process of searching the image the most similar to given image in image data base.
3. according to claim 2 a kind of at ray image retrieval system, it is characterized in that: described image is for the overall situation, image is comprised of by certain semantic association relation (cluster index) all image blocks, and similar image has the identical cluster index that quantity does not wait.
4. according to claim 3 a kind of at ray image retrieval system, it is characterized in that: described cluster is on the basis that image is cut apart and assemblage characteristic extracts, adopt the k-means clustering algorithm based on Map/Reduce to implement cluster to image block.
5. according to claim 3 a kind of at ray image retrieval system, it is characterized in that: described cluster is that image block to be clustered is stored on different back end dispersedly in its bottom layer realization, each piece can also copy some parts, be stored on different back end, to reach fault-tolerant object, the back end in cluster is in charge of the storage on its place node.
6. according to claim 3 a kind of at ray image retrieval system, it is characterized in that: described image retrieval is on the basis of image block cluster, regard image block cluster index as vision keyword, all vision keywords have formed vision keyword dictionary, and image is by the feature clustering vector representation consisting of a series of vision keyword.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201310161562.XA CN104142920A (en) | 2013-05-06 | 2013-05-06 | Online image retrieval system |
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| CN201310161562.XA CN104142920A (en) | 2013-05-06 | 2013-05-06 | Online image retrieval system |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104778281A (en) * | 2015-05-06 | 2015-07-15 | 苏州搜客信息技术有限公司 | Image index parallel construction method based on community analysis |
| CN109074659A (en) * | 2016-05-04 | 2018-12-21 | 皇家飞利浦有限公司 | Medical image resources registration |
| CN110297935A (en) * | 2019-06-28 | 2019-10-01 | 京东数字科技控股有限公司 | Image search method, device, medium and electronic equipment |
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| CN103077253A (en) * | 2013-01-25 | 2013-05-01 | 西安电子科技大学 | High-dimensional mass data GMM (Gaussian Mixture Model) clustering method under Hadoop framework |
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| US20050004897A1 (en) * | 1997-10-27 | 2005-01-06 | Lipson Pamela R. | Information search and retrieval system |
| CN102508909A (en) * | 2011-11-11 | 2012-06-20 | 苏州大学 | Image retrieval method based on multiple intelligent algorithms and image fusion technology |
| CN103077253A (en) * | 2013-01-25 | 2013-05-01 | 西安电子科技大学 | High-dimensional mass data GMM (Gaussian Mixture Model) clustering method under Hadoop framework |
| CN103064991A (en) * | 2013-02-05 | 2013-04-24 | 杭州易和网络有限公司 | Mass data clustering method |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104778281A (en) * | 2015-05-06 | 2015-07-15 | 苏州搜客信息技术有限公司 | Image index parallel construction method based on community analysis |
| CN109074659A (en) * | 2016-05-04 | 2018-12-21 | 皇家飞利浦有限公司 | Medical image resources registration |
| CN109074659B (en) * | 2016-05-04 | 2023-04-04 | 皇家飞利浦有限公司 | Medical atlas registration |
| CN110297935A (en) * | 2019-06-28 | 2019-10-01 | 京东数字科技控股有限公司 | Image search method, device, medium and electronic equipment |
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