A Feature Extraction from Compressed Multimedia Data For Content-based Multimedia Data Retrieval
| Project/Area Number |
11680413
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
情報システム学(含情報図書館学)
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| Research Institution | University of Electro-Communications |
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Principal Investigator |
HOSHI Mamoru University of Electro-Communications, Graduate School of Information Systems, Professor., 大学院・情報システム学研究科, 教授 (80125955)
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| Co-Investigator(Kenkyū-buntansha) |
ONISHI Kensuke University of Electro-Communications, Graduate School of Information Systems, Research Associate., 大学院・情報システム学研究科, 助手 (00303024)
OHMORI Tadashi University of Electro-Communications, Graduate School of Information Systems, Assistant Professor., 大学院・情報システム学研究科, 助教授 (30233274)
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| Project Period (FY) |
1999 – 2000
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| Project Status |
Completed (Fiscal Year 2000)
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| Budget Amount *help |
¥2,800,000 (Direct Cost: ¥2,800,000)
Fiscal Year 2000: ¥1,200,000 (Direct Cost: ¥1,200,000)
Fiscal Year 1999: ¥1,600,000 (Direct Cost: ¥1,600,000)
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| Keywords | Multimedia Database / Content-based Retrieval / indexing / Feature Extraction / MPEG4 / audio / 内容に基づく類似検索 / 特徴量の抽出 / 圧縮データ / 特微抽出 / MPEG-4 / TwinVQ |
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| Research Abstract |
Multimedia Database(MDB)is a fundamental and important system to manage large set of multimedia data. There are two problems to make an effective MDB.One is how to efficiently store multimedia data, another is how to effectively retrieve multimedia. For solving these problems, we propose a simple and clear framework for a content-based multimedia data retrieval system using MPEG4 domain and provide a new music feature for a content-based music retrieval on the new framework. A big difference between the new framework of a content-based multimedia data retrieval system and a typical framework of that is a feature extraction method. On the new framework, a feature for describing a content of multimedia data is extracted from the compressed data or a compression method, while on a typical framework a feature is extracted from row data(e.g.raw image data, signal data of music, etc.).
We target on a content-based music retrieval system on the new framework and make a prototype system which a
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llows us queries by example of the compressed music data whose bit rate are various. For making a content-based music retrieval system, we provide a new music feature which satisfies the following requirements ;
1. computable from MPEG-4/audio domain,
2. robust for bit rate,
3. computable from the compressed data without decoding.
We show theoretically that the i-th autocorrelation coefficient with bit rate B_1 can approximate the j-th autocorrelation coefficient with bit rate B_2, where j=[(B_1)/(B_2)i]. So, we call a relation of autocorrelation coefficients with respect to bit rates an approximation relation with bit rate. And we experimentally confirm that the extracted autocorrelation coefficients from MPEG4/audio domain actually satisfy the approximation relation with bit rate.
Then, we provide a new music feature by using the approximation relation with bit rate, and experiment on the music retrievals to evaluate the performance of retrievals by using the music feature with various bit rates. The results of experiments show a good performance of retrievals, and that the proposed music feature have a good performance of retrieval for queries with various bit rate. Less
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Report
(3 results)
Research Products
(13 results)
