Ensemble of Deep Neural Netwoks for Image Analysi | Posted on:2019-07-29 | Degree:M.C.Sc | Type:Thesis | University:Lamar University - Beaumont | Candidate:Rijal, Nabin Sharma | Full Text:PDF | GTID:2448390002493288 | Subject:Computer Science | Abstract/Summary: | | 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. | Keywords/Search Tags: | Image, Deep neural, Cnns, Dataset, Approach | | Related items |
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