A Clonal Selection Algorithm with Levenshtein Distance based Image Similarity in Multidimensional Subjective Tourist Information and Discovery of Cryptic Spots by Interactive GHSOM

TL;DR

This paper introduces a novel approach combining Levenshtein distance with a clonal selection algorithm and interactive GHSOM to classify and discover cryptic tourist spots using subjective image data from mobile sensing.

Contribution

It proposes a new method integrating Levenshtein distance with clonal selection and GHSOM for image-based tourist data classification and cryptic spot discovery.

Findings

Effective classification of subjective image data achieved

Levenshtein distance improves image viewpoint proximity detection

Cryptic tourist spots can be discovered through the proposed method

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.