SUPER PIXEL EXTRACTION VIA CONVEXITY INDUCED BOUNDARY ADAPTATION

Date

2013-07-19

Author

Tasli, H. Emrah
Cigla, Cevahir
Gevers, Theo
Alatan, Abdullah Aydın

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

214
views

0
downloads

This study presents an efficient super-pixel extraction algorithm with major contributions to the state-of-the-art in terms of accuracy and computational complexity. Segmentation accuracy is improved through convexity constrained geodesic distance utilization; while computational efficiency is achieved by replacing complete region processing with boundary adaptation idea. Starting from the uniformly distributed rectangular equal-sized super-pixels, region boundaries are adapted to intensity edges iteratively by assigning boundary pixels to the most similar neighboring super-pixels. At each iteration, super-pixel regions are updated and hence progressively converging to compact pixel groups. Experimental results with state-of-the-art comparisons, validate the performance of the proposed technique in terms of both accuracy and speed.

Subject Keywords

Convexity constraint, Image segmentation

URI

https://hdl.handle.net/11511/53479

Conference Name

IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

MRF Based Image Segmentation Augmented with Domain Specific Information Karadag, Ozge Oztimur; Yarman Vural, Fatoş Tunay (2013-09-13) A Markov Random Field based image segmentation system which combines top-down and bottom-up segmentation approaches is proposed in this study. The system is especially proposed for applications where no labeled training set is available, but some priori general information referred as domain specific information about the dataset is available. Domain specific information is received from a domain expert and formalized by a mathematical representation. The type of information and its representation depends o...
A Feature Extraction Method for Marble Tile Classification DEVİREN, Murat; M KORAY, Balcı; Leloğlu, Uğur Murat; SEVERCAN, Mete (2000-03-03) This study focuses on a feature extraction algorithm for classification of marble tiles. The color content and vein distribution are considered to be the main criteria for classification. A color segmentation algorithm is used for detection of veins. The shape analysis of the veins are done by utilizing the distance image.
HYPERSPECTRAL UNMIXING BASED VEGETATION DETECTION WITH SEGMENTATION Özdemir, Okan Bilge; Soydan, Hilal; Çetin, Yasemin; Duzgun, Sebnem (2016-07-15) This paper presents a vegetation detection application with semi-supervised target detection using hyperspectral unmixing and segmentation algorithms. The method firstly compares the known target spectral signature from a generic source such as a spectral library with each pixel of hyperspectral data cube employing Spectral Angle Mapper (SAM) algorithm. The pixel(s) with the best match are assumed to be the most likely target vegetation locations. The regions around these potential target locations are furt...
Selection and Fusion of Multiple Stereo Algorithms for Accurate Disparity Segmentation Bilgin, Arda; Ulusoy, İlkay (2009-01-01) Fusion of multiple stereo algorithms is performed in order to obtain accurate disparity segmentation in this study. Reliable disparity map of real-time stereo images is estimated and disparity segmentation is performed for object detection purpose. First, stereo algorithms which have high performance in real-time applications are chosen among the algorithms in the literature and three of them are implemented. Then, the results of these algorithms are fused to gain better performance in disparity estimation....
Visual concept detection by stacked generalization Tekin, Mashar; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2014) In this thesis, we propose a new Stacked Generalization method, called Fuzzy Stacked Generalized Ranking Optimizer, to optimize the ranking performances of visual concept detection systems. In the proposed method, fuzzy k-NN classifiers are employed in the base-layer. Then, a classifier selection algorithm is employed to select the classifiers which will be combined in meta-layer. Finally, the results of the selected classifiers are combined and classified by a fuzzy k-NN meta classifier. In the experiments...

Citation Formats

H. E. Tasli, C. Cigla, T. Gevers, and A. A. Alatan, “SUPER PIXEL EXTRACTION VIA CONVEXITY INDUCED BOUNDARY ADAPTATION,” presented at the IEEE International Conference on Multimedia and Expo Workshops (ICMEW), San Jose, CA, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53479.