| Budget Amount *help |
¥26,520,000 (Direct Cost: ¥20,400,000、Indirect Cost: ¥6,120,000)
Fiscal Year 2014: ¥8,710,000 (Direct Cost: ¥6,700,000、Indirect Cost: ¥2,010,000)
Fiscal Year 2013: ¥11,310,000 (Direct Cost: ¥8,700,000、Indirect Cost: ¥2,610,000)
Fiscal Year 2012: ¥6,500,000 (Direct Cost: ¥5,000,000、Indirect Cost: ¥1,500,000)
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| Outline of Final Research Achievements |
The goal of our research is the construction of the super multi-class generic object recognition system by learning the relationship between a large amount of image and text data statistically. A method to continuously learn the classifiers from a huge amount of data without breakdown is crucial to realize this system. If there are many objects in one image, it is important to recognize where and what they are. A cost to construct high quality training dataset is so expensive that reducing the construction cost is also crucial. Moreover, a technique to find novel classes is a bottleneck for the continuously growing recognition system. In this research, we have tackled the above mentioned topics and produced some results.
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