| Project/Area Number |
24700179
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Ritsumeikan University |
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Principal Investigator |
HAN Xian-Hua 立命館大学, 立命館グローバル・イノベーション研究機構, 准教授 (60469195)
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| Co-Investigator(Renkei-kenkyūsha) |
CHEN Yen-Wei 立命館大学, 情報理工学部, 教授 (60236841)
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| Project Period (FY) |
2012-04-01 – 2015-03-31
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| Project Status |
Completed (Fiscal Year 2014)
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| Budget Amount *help |
¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2014: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2013: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2012: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
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| Keywords | 画像認識 / 多次元多様体学習 / データ駆動モデル / 局所特徴 / テンソル解析 / 国際情報交換 |
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| Outline of Final Research Achievements |
In this project, we propose to represent an image as a local descriptor tensor and use a Multilinear Supervised Neighborhood Embedding (MSNE) for discriminant feature extraction, which is able to be used for subject or scene recognition. The contributions of our project include: (1) a image representation framework denoted as local descriptor tensor, which can effectively combine a moderate amount of local features together for image representation and be more efficient than the popular existing Bag-Of-Feature model; (2) a MSNE analysis algorithm, which can directly deal with the local descriptor tensor for extracting discriminant and compact features, and at the same time preserve neighborhood structure in tensor space; (3) a data-driven modeling procedure for raw features instead of the hand-craft local descriptors such as SIFT, SURF. We demonstrate the performance advantages of our proposed approach over existing techniques on understanding different types of benchmark database.
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