کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6864714 | 1439550 | 2018 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Multi-modal local receptive field extreme learning machine for object recognition
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
Learning rich representations efficiently plays an important role in the multi-modal recognition task, which is crucial to achieving high generalization performance. To address this problem, in this paper, we propose an effective Multi-Modal Local Receptive Field Extreme Learning Machine (MM-LRF-ELM) structure, while maintaining ELM's advantages of training efficiency. In this structure, LRF-ELM is first conducted for feature extraction for each modality separately. And then, the shared layer is developed by combining these features from each modality. Finally, the Extreme Learning Machine (ELM) is used as supervised feature classifier for the final decision. Experimental validation on Washington RGB-D Object Dataset illustrates that the proposed multiple modality fusion method achieves better recognition performance.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 277, 14 February 2018, Pages 4-11
Journal: Neurocomputing - Volume 277, 14 February 2018, Pages 4-11
نویسندگان
Huaping Liu, Fengxue Li, Xinying Xu, Fuchun Sun,