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A Bayesian nonparametric model for hierarchical sequence of images

Version 2 2024-06-17, 13:14
Version 1 2015-03-09, 23:25
conference contribution
posted on 2024-06-17, 13:14 authored by Y Qiu, X Sun, MF She
Scale features are useful for a great number of applications in computer vision. However, it is difficult to tolerate diversities of features in natural scenes by parametric methods. Empirical studies show that object frequencies and segment sizes follow the power law distributions which are well generated by Pitman-Yor (PY) processes. Based on mid-level segments, we propose a hierarchical sequence of images to obtain scale information stored in a hierarchical structure through the hierarchical Pitman-Yor (HPY) model which is expected to tolerate uncertainty of natural images. We also evaluate our representation by the application of segmentation.

History

Pagination

1-6

Location

Hangzhou City, China

Start date

2014-10-18

End date

2014-10-19

Language

eng

Publication classification

E Conference publication, E1 Full written paper - refereed

Copyright notice

2014, DEStech Publications

Editor/Contributor(s)

[Unknown]

Title of proceedings

CSSE 2014 : Proceedings of the 2014 International Conference on Computer Science and Software Engineering

Event

Computer Science and Software Engineering. Conference ( 2014: Hangzhou City, China)

Publisher

DEStech Publications

Place of publication

[Hangzhou City, China]

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