Feature-Agnostic Low-Cost Place Recognition for Appearance-Based Mapping

Kazmi, S. M. Ali Musa; Mohamed, Mostafa; Mertsching, Bärbel

doi:10.1007/978-3-030-34995-0_5

Cited by 1 publication

(1 citation statement)

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“…Many scholars have conducted lots of research on image processing of tourist attractions. Based on image processing, some mined the semantics of tourist attractions, some classified the images on tourist attractions, some recommended tourist attractions, and some planned travel routes [6,7]. Through probabilistic latent semantic analysis (PLSA), Khaliq et al [8] optimized three semantic-based classification methods for tourist photos, and explored the association between image index semantics, geographical location of tourist attractions, and text labels.…”

Section: Introductionmentioning

confidence: 99%

Location Identification and Personalized Recommendation of Tourist Attractions Based on Image Processing

Zhang¹,

Liu²,

Liu³

et al. 2021

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Currently, tourists tend to plan travel routes and itineraries by searching for relevant information on tourist attractions via the Internet and intelligent terminals. However, it is difficult to achieve good retrieval effect on tourist attraction images with text labels. Based on deep learning, the visual location identification faces such defects as frequent mismatching, high probability of weak matching, and long execution time. To solve these defects, this paper puts forward a novel method for location identification and personalized recommendation of tourist attractions based on image processing. Specifically, the authors detailed the ideas and steps of the location identification algorithm for tourist attractions. The algorithm, grounded on hash retrieval, encompasses two stages: an offline stage, and an online stage. Besides, a personalized recommendation model for tourist attractions based on geographical location and time period. Finally, the proposed algorithm and model were proved accurate and effective through experiments.

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Section: Introductionmentioning

confidence: 99%