Analyzing Movie Posters using DBSCAN Technique Procedia Computer Science
Article Sidebar
Main Article Content
Abstract
This study uses Image Processing and Computer Vision techniques to analyze movie posters. It use Image Clustering and Image Augmentation to distinguish factors that influence the decision to watch a movie. The analysis uses DBSCAN and Resnet50 to classify data, and Dimension Reduction and Umap to reduce data size. The results of the analysis highlight the appeal of movie posters and indicate the factors that influence interest and decision-making in movie viewing.
The survey and analysis results found that the characteristics of movie posters are significant in assisting viewers’ decision-making, making them more rational in choosing movies and increasing their satisfaction in watching movies.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The content within the published articles, including images and tables, is copyrighted by Rajamangala University of Technology Rattanakosin. Any use of the article's content, text, ideas, images, or tables for commercial purposes in various formats requires permission from the journal's editorial board.
Rajamangala University of Technology Rattanakosin permits the use and dissemination of article files under the condition that proper attribution to the journal is provided and the content is not used for commercial purposes.
The opinions and views expressed in the articles are solely those of the respective authors and are not associated with Rajamangala University of Technology Rattanakosin or other faculty members in the university. The authors bear full responsibility for the content of their articles, including any errors, and are responsible for the content and editorial review. The editorial board is not responsible for the content or views expressed in the articles.
References
Sehgal, R., Gupta, N., Tomar, A., Sharma, M. D., & Kumaran, V. (2022). Smart Electrical and Mechanical Systems.
คอมพิวเตอร์วิทัศน์คืออะไร. (2023). สืบค้นวันที่ 11 กันยายน 2566, จาก https://aws.amazon.com/th/what-is/computer-vision/
What is Clustering?. (2022). สืบค้นวันที่ 6 พฤศจิกายน 2566, จาก https://developers.google.com/machine-learning/clustering/overview
Image Augmentation. (2021). สืบค้นวันที่ 6 พฤศจิกายน 2566, จาก https://www.analyticsvidhya.com/blog/2021/03/image-augmentation-techniques-for-training-deep-learning-models/
Tran, T. N., Drab, K., & Daszykowski, M. (2012). Revised DBSCAN algorithm to cluster data with dense adjacent clusters.
Understanding ResNet50 architecture. (2012). สืบค้นวันที่ 7 พฤศจิกายน 2566, จาก https://iq.opengenus.org/resnet50-architecture/
Introduction to Dimensionality Reduction. (2023). สืบค้นวันที่ 20 พฤศจิกายน 2566, จาก
https://www.geeksforgeeks.org/dimensionality-reduction/
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. (2018). สืบค้นวันที่ 20 พฤศจิกายน 2566, จาก https://umap-learn.readthedocs.io/en/latest/