Hierarchical Feature Mining for Image Classification and Segmentation

Yousif, Hayder and Al-Milaji, Zahraa (2022) Hierarchical Feature Mining for Image Classification and Segmentation. In: IMDC-IST 2021, 7-9 September 2021, Sakarya, Turkey.

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In this paper, supervised and unsupervised learning are used to classify and segment two different types of images. Specifically, first, we use our deep learning classification model to develop a fast and accurate scheme for feature extraction. We design a DCNN model that is 18 times faster than the state-of-the-art AlexNet at a small loss of accuracy. Second, different classifiers have been trained on the features that are extracted from the fully connected layer of the deep convolutional neural networks (DCNN). Finally, we perform K-means-graph-cut segmentation and qual- iteratively compare with supervised segmentation results. Image-level and region-level classification has been studied by utilizing natural scenes and biomedical image processing, respectively. Our experimental results demonstrate that the proposed method outperforms existing methods on a biomedical image dataset.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: dcnn supervised learning unsupervised learning k-means graph- cut
Subjects: T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 29 Apr 2022 12:37
Last Modified: 29 Apr 2022 12:37
URI: https://eprints.eudl.eu/id/eprint/10884

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