Fast Image Categorization Method using Scene-Context Scale Information
| Project/Area Number |
23500237
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Tokyo Polytechnic University |
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Principal Investigator |
KANG Yousun 東京工芸大学, 工学部, 准教授 (10582893)
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| Project Period (FY) |
2011 – 2013
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2013: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2012: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2011: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 画像分類 / 画像セグメンテーション / シーンコンテキストスケール / 画像自動分類 / マルチカーネル学習 / ロボットビジョン / コンピュータビジョン / コンテキスト情報 / 画像認識 / 情報システム / 人工知能 / 知覚情報処理 / 知能ロボティクス |
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| Research Abstract |
We propose scale-optimized textons to learn the best scale for each object in a scene. We incorporate them into image categorization and semantic segmentation. Our textonization module produces a scale-optimized codebook of visual words. We approach the scale-optimization problem of textons using the scene-context scale in each image, which is the effective scale of local context to classify an image pixel in a scene. We perform the textonization process using a randomized decision forest, which is a powerful tool with high computational efficiency in vision applications. Results of our experiments using MSRC21 and VOC 2007 segmentation datasets demonstrate that our scale-optimized textons improve image categorization and segmentation performance.
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Report
(4 results)
Research Products
(27 results)
