The appearance of the giant component in descriptor graphs and its application for descriptor selection

Authors:
Anita Keszler;Levente Kovács;Tamás Szirányi
Affiliations:
Distributed Events Analysis Research Laboratory, Computer and Automation Research Institute, Hungarian Academy of Sciences, Budapest, Hungary;Distributed Events Analysis Research Laboratory, Computer and Automation Research Institute, Hungarian Academy of Sciences, Budapest, Hungary;Distributed Events Analysis Research Laboratory, Computer and Automation Research Institute, Hungarian Academy of Sciences, Budapest, Hungary
Venue:
CLEF'12 Proceedings of the Third international conference on Information Access Evaluation: multilinguality, multimodality, and visual analytics
Year:
2012

Citing 8
Cited 0

Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Focus Area Extraction by Blind Deconvolution for Defining Regions of Interest

IEEE Transactions on Pattern Analysis and Machine Intelligence
Sort-merge feature selection and fusion methods for classification of unstructured video

ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
A semi-automatic feature selecting method for sports video highlight annotation

VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Local-Learning-Based Feature Selection for High-Dimensional Data Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Distribution Based Quick Image Retrieval

WISA '10 Proceedings of the 2010 Seventh Web Information Systems and Applications Conference
Feature selection for automatic image annotation

DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Color and texture descriptors

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper presents a random graph based analysis approach for evaluating descriptors based on pairwise distance distributions on real data. Starting from the Erdős-Rényi model the paper presents results of investigating random geometric graph behaviour in relation with the appearance of the giant component as a basis for choosing descriptors based on their clustering properties. Experimental results prove the existence of the giant component in such graphs, and based on the evaluation of their behaviour the graphs, the corresponding descriptors are compared, and validated in proof-of-concept retrieval tests.