by Shin, B-S, Russell, J and Klette, R
Abstract:
We propose a method to extract and classify insect footprints for the purpose of recognition. Our four-level procedural feature extraction model is defined as follows: First, images produce new data via the trace transform. Second, for reducing the dimensionality of the produced data, we apply some mathematical conversions. Third, dimensionality-reduced data are converted into frequency components. Finally, characteristic signals with significant components of representative values are created by excluding insignificant factors such as those related to noise. For classification, based on uncertain features, we propose a decision method defined by fuzzy weights and a fuzzy weighted mean. The proposed fuzzy weight decision method estimates weights according to degrees of contribution. Weights are assigned by ranking the degree of a feature’s contribution. We present experimental results of classification by using the proposed method on scanned insect footprints. Experiments show that the proposed method is suitable for noisy footprints with irregular directions, or symmetrical patterns in the extracted segments. © 2012 Springer-Verlag.
Reference:
Feature extraction and classification for insect footprint recognition (Shin, B-S, Russell, J and Klette, R), In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 7441 LNCS, 2012.
Bibtex Entry:
@inproceedings{shin2012featurerecognition,
author = "Shin, B-S and Russell, J and Klette, R",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "196--203",
title = "Feature extraction and classification for insect footprint recognition",
volume = "7441 LNCS",
year = "2012",
abstract = "We propose a method to extract and classify insect footprints for the purpose of recognition. Our four-level procedural feature extraction model is defined as follows: First, images produce new data via the trace transform. Second, for reducing the dimensionality of the produced data, we apply some mathematical conversions. Third, dimensionality-reduced data are converted into frequency components. Finally, characteristic signals with significant components of representative values are created by excluding insignificant factors such as those related to noise. For classification, based on uncertain features, we propose a decision method defined by fuzzy weights and a fuzzy weighted mean. The proposed fuzzy weight decision method estimates weights according to degrees of contribution. Weights are assigned by ranking the degree of a feature's contribution. We present experimental results of classification by using the proposed method on scanned insect footprints. Experiments show that the proposed method is suitable for noisy footprints with irregular directions, or symmetrical patterns in the extracted segments. © 2012 Springer-Verlag.",
doi = "10.1007/978-3-642-33275-3_24",
isbn = "9783642332746",
issn = "0302-9743",
eissn = "1611-3349",
language = "eng",
}