A Clonal Selection Algorithm with Levenshtein Distance based Image Similarity in Multidimensional Subjective Tourist Information and Discovery of Cryptic Spots by Interactive GHSOM
April 08, 2018 Β· Declared Dead Β· π IEEE International Conference on Systems, Man and Cybernetics
"No code URL or promise found in abstract"
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Authors
Takumi Ichimura, Shin Kamada
arXiv ID
1804.05669
Category
cs.IR: Information Retrieval
Cross-listed
cs.CV,
cs.SI,
eess.IV
Citations
4
Venue
IEEE International Conference on Systems, Man and Cybernetics
Last Checked
4 months ago
Abstract
Mobile Phone based Participatory Sensing (MPPS) system involves a community of users sending personal information and participating in autonomous sensing through their mobile phones. Sensed data can also be obtained from external sensing devices that can communicate wirelessly to the phone. Our developed tourist subjective data collection system with Android smartphone can determine the filtering rules to provide the important information of sightseeing spot. The rules are automatically generated by Interactive Growing Hierarchical SOM. However, the filtering rules related to photograph were not generated, because the extraction of the specified characteristics from images cannot be realized. We propose the effective method of the Levenshtein distance to deduce the spatial proximity of image viewpoints and thus determine the specified pattern in which images should be processed. To verify the proposed method, some experiments to classify the subjective data with images are executed by Interactive GHSOM and Clonal Selection Algorithm with Immunological Memory Cells in this paper.
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