Generic Object Recognition with Local Receptive Fields Based Extreme Learning Machine
Generic object recognition is to classify the object to a generic category. Intra-class variabilities cause big troubles for this task. Traditional methods involve plenty of pre-processing steps, like model construction, feature extraction, etc. Moreover, these methods are only effective for some sp...
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| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/81196 http://hdl.handle.net/10220/39168 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Generic object recognition is to classify the object to a generic category. Intra-class variabilities cause big troubles for this task. Traditional methods involve plenty of pre-processing steps, like model construction, feature extraction, etc. Moreover, these methods are only effective for some specific dataset. In this paper, we propose to use local receptive fields based extreme learning machine (ELM-LRF) as a general framework for object recognition. It is operated directly on the raw images and thus suitable for all different datasets. Additionally, the architecture is simple and only requires few computations, as most connection weights are randomly generated. Comparing to state-of-the-art results on NORB, ETH-80 and COIL datasets, it is on par with the best one on ETH-80 and sets the new records for NORB and COIL. |
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