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Your search keyword '"Sirin, Yahya"' showing total 16 results
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16 results on '"Sirin, Yahya"'

1. Web page classification with Google Image Search results

Author

Aydos, Fahri, Özbayoğlu, A. Murat, Şirin, Yahya, and Demirci, M. Fatih

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

In this paper, we introduce a novel method that combines multiple neural network results to decide the class of the input. This is the first study which used the method for web pages classification. In our model, each element is represented by multiple descriptive images. After the training process of the neural network model, each element is classified by calculating its descriptive image results. We apply our idea to the web page classification problem using Google Image Search results as descriptive images. We obtained a classification rate of 94.90% on the WebScreenshots dataset that contains 20000 web sites in 4 classes. The method is easily applicable to similar problems.

Published
2020

13. 2D and 3D shape retrieval using skeleton filling rate.

Author

Sirin, Yahya and Demirci, M.

Subjects
SHAPE recognition (Computer vision), DIGITAL images, IMAGE retrieval, COMPUTER algorithms, SKELETON
Abstract

As an increasing number of digital images are generated, a demand for an efficient and effective image retrieval mechanisms grows. In this work, we present a new skeleton-based algorithm for 2D and 3D shape retrieval. The algorithm starts by drawing circles (spheres for 3D) of increasing radius around skeletons. Since each skeleton corresponds to the center of a maximally inscribed circle (sphere), this process results in circles (spheres) that are partially inside the shape. Computing the ratio between pixels that lie within the shape and the total number of pixels allows us to distinguish shapes with similar skeletons. Experimental evaluation of the proposed approach including a comprehensive comparison with the previous techniques demonstrates both effectiveness and robustness of our algorithm for shape retrieval using several 2D and 3D datasets. [ABSTRACT FROM AUTHOR]

Published
2017
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