Ensemble of Deep Neural Netwoks for Image Analysis
Başlık:
Ensemble of Deep Neural Netwoks for Image Analysis
Yazar:
Rijal, Nabin Sharma, author.
ISBN:
9780438137752
Yazar Ek Girişi:
Fiziksel Tanımlama:
1 electronic resource (56 pages)
Genel Not:
Source: Masters Abstracts International, Volume: 57-06M(E).
Advisors: Jing Zhang Committee members: Kami Makki; Sujing Wang.
Özet:
Deep learning has been able to achieve impressive results in recent years. But the availability of suitable amount of domain-specific data remains a challenge especially in image related tasks where dataset can vary in size from a few hundred images to millions of images. A sufficiently deep neural network with millions of parameters needs huge amount of data for training. On the other hand, training deep neural networks on a large dataset is computationally expensive. In this context, this thesis explores an ensemble learning based approach for image recognition. Multiple pre-trained Convolution Neural Networks (CNNs) are fine-tuned as base learners, and they are combined using a meta learner to improve the overall performance. This approach provides reasonable accuracy with comparatively less computational cost. As part of this study, a novel regression model is developed with 3 CNNs as base learners and a feed-forward neural network as meta-learner to predict the value of fine particulate matter (PM2.5) from image using a small image dataset. The experimental results demonstrate that the proposed method provides a more accurate PM 2.5 prediction compared to the individual CNNs and therefore it can be used for image-based PM2.5 estimation. A relatively similar approach is applied for a classification task with six CNNs as base learners using a large image dataset. In this case also, the ensemble-based approach outperforms the individual CNNs in terms of classification accuracy.
Notlar:
School code: 0424
Konu Başlığı:
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Yer Numarası | Demirbaş Numarası | Shelf Location | Lokasyon / Statüsü / İade Tarihi |
---|---|---|---|
XX(692008.1) | 692008-1001 | Proquest E-Tez Koleksiyonu | Arıyor... |
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