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Analysing various methods for object extraction and the classification of kitchens

Essenstam, L. (2019) Analysing various methods for object extraction and the classification of kitchens.

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Abstract:In the last few years there have been great successes in the application of deep and machine learning for the use of both object detection and classification. However, when there is a limited amount of data available for many different classes, accuracy is low and decent results can often not be obtained. This research aims to show various case-specific methods to analyse the data and to extract important features to improve classification, such as the Hough transform and mean shift segmentation. A convolution neural network, Alexnet has been trained using both the raw data and the extracted features. When training and validating the network using the raw data an accuracy of 28% has been obtained. When applying extracted features, the handles of the kitchen, to the same network accuracy improved from a 28% to an accuracy of 41%. This increase of thirteen percentage points shows that significant improvement is possible when extracting features before training a network.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:53 electrotechnology, 54 computer science
Programme:Electrical Engineering BSc (56953)
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