Wild Image Retrieval with HAAR Features and Hybrid DBSCAN Clustering For 3D Cultural Artefact Landmarks Reconstruction

• Autorzy: Pitchandi Perumal

Identyfikatory

DOI

10.12913/22998624/150320

• Języki publikacji: EN

Abstrakty

EN

In this digital age large amounts of information, images and videos can be found in the web repositories which accumulate this information. These repositories include personal, historic, cultural, and business event images. Image mining is a limited field in research where most techniques look at processing images instead of mining. Very limited tools are found for mining these images, specifically 3D (Three Dimensional) images. Open source image datasets are not structured making it difficult for query based retrievals. Techniques extracting visual features from these datasets result in low precision values as images lack proper descriptions or numerous samples exist for the same image or images are in 3D. This work proposes an extraction scheme for retrieving cultural artefact based on voxel descriptors. Image anomalies are eliminated with a new clustering technique and the 3D images are used for reconstructing cultural artefact objects. Corresponding cultural 3D images are grouped for a 3D reconstruction engine’s optimized performance. Spatial clustering techniques based on density like PVDBSCAN (Particle Varied Density Based Spatial Clustering of Applications with Noise) eliminate image outliers. Hence, PVDBSCAN is selected in this work for its capability to handle a variety of outliers. Clustering based on Information theory is also used in this work to identify cultural object’s image views which are then reconstructed using 3D motions. The proposed scheme is benchmarked with DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to prove the proposed scheme’s efficiency. Evaluation on a dataset of about 31,000 cultural heritage images being retrieved from internet collections with many outliers indicate the robustness and cost effectiveness of the proposed method towards a reliable and just-in-time 3D reconstruction than existing state-of-the-art techniques.

Słowa kluczowe

EN

outliers removal; culturalartefact objects; 3D reconstruction; particle swarm optimization; PSO; spatial clustering; density based spatial clustering; noise clustering algorithm

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2022
• Tom: Vol. 16, no 3
• Strony: 269--281
• Opis fizyczny: Bibliogr. 27 poz., fig.

Twórcy

autor

Pitchandi Perumal

• Department of Computer Science and Engineering, Sri Ramakrishna Engineering College, Coimbatore, India

• https://orcid.org/0000-0003-0121-8936

Bibliografia

• 1. Google Art Project, http://www.googleartproject.com/.
• 2. Grand Versailles Numérique, http://www.gvn.chateauversailles.fr.
• 3. The Khufu Pyramid, http://www.3dvia.com/3d_experiences/view_experience.php?experienceId=1.
• 4. Bunsch E., Sitnik R. and Michoński J. Art documentation quality in function of 3D scanning resolution and precision. In: Proc. SPIE 7869, 2011.
• 5. Bunsch E. and Sitnik R. Documentation instead of visualization – applications of 3D scanning in works of art analysis. In: Proc. SPIE 7531, 2010.
• 6. Pavlidisa G., Koutsoudisa A., Arnaoutogloua F., Tsioukasb V. and Chamzas Ch.. Methods for 3D digitization of cultural artefact. Journal of Cultural artefact, 2007; 8(1): pp. 93–98.
• 7. Glinkowski W., Michonski J., Sitnik R. and Witkowski M. 3D diagnostic system for anatomical structures detection based on a parameterized method of body surface analysis. Information Technologies in Biomedicine, Advances in Intelligent and Soft Computing, 2010; 69, 153–164.
• 8. Zhao Lei and Duan Qing Xu. Immersive display and interactive techniques for the modeling and rendering of virtual heritage environments. In: International Conference on Information Management and Engineering, 2009, 601–606.
• 9. Barone S., Paoli A. and Razionale A.V. 3D virtual reconstructions of artworks by a multiview scanning process. In: 18th International Conference on Virtual Systems and Multimedia (VSMM). 2012, 259–265.
• 10. Karaszewski M., Sitnik R., Bunsch E. On-line, collision-free positioning of a scanner during fullyautomated three-dimensional measurement of cultural artefact objects. Robotics and Autonomous Systems, 2012; 60(9):1205–1219.
• 11. Sitnik R, and Karaszewski M. Automated Processing of Data from 3D Scanning of Cultural artefact Objects. In: Proceedings of the Third international conference on Digital heritage, 2010.
• 12. Ioannides M., Fellner D., Georgopoulos A., Hadjimitsis D.G. (Eds). Digital Heritage. In: 3rd International Conference dedicated on Digital Heritage. 2010: 28–41.
• 13. Bay H., Tuytelaars T. and Gool L.V. SURF: Speeded up Robust Features. In: Computer VisionECCV 2006. 2006, 404–417.
• 14. Wu C., Agarwal S., Curless B. and Seitz S.M. Multicore bundle adjustment. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2011,3057–3064.
• 15. Wu Doulamis, A. Automatic 3D Reconstruction From Unstructured Videos Combining Video Summarization and Structure From Motion. Frontiers in ICT, 2018, 5, 29.
• 16. Cao J., Wang M., Shi H., Hu G., & Tian Y. A new approach for large-scale scene image retrieval based on improved parallel-means algorithm in mapreduce environment. Mathematical Problems in Engineering, 2016.
• 17. Condorelli F., Rinaudo F., Processing historical film footage with Photogrammetry and Machine Learning for Cultural Heritage documentation. In: ACM Multimedia Conference Proceedings, October 2019, Nice, France. ACM, NY, USA. 8 pages. https://doi.org/10.1145/3347317.3357248
• 18. Pan J., Li L., Yamaguchi H., Hasegawa K., Thufail F.I. & Tanaka S. 3D reconstruction of Borobudur reliefs from 2D monocular photographs based on soft-edge enhanced deep learning. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 183, 439–450.
• 19. Makantasis K., Doulamis A., Doulamis N. & Ioannides M. In the wild image retrieval and clustering for 3D cultural heritage landmarks reconstruction. Multimedia Tools and Applications, 2016, 75(7), 3593–3629.
• 20. Kekre D.H.B., Sarode T.K., Thepade S.D. and Vaishali V. Improved texture feature based image retrieval using Kekre’s fast codebook generation algorithm. In: Pise S.J. (Ed.) Thinkquest~2010, Springer, India, 2011.
• 21. Simon I., Snavely N. and Seitz S.M. Scene Summarization for Online Image Collections. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, 1–8.
• 22. He X., W.-Y. Ma, and H.-J. Zhang. Learning an Image Manifold for Retrieval. In: Proc. ACM Multimedia, 2004.
• 23. Zheng Y-T, Zhao M, Song Y, Adam H, Buddemeier U, Bissacco A, Brucher F, Chua T-S and Neven H. Tour the world: Building a web-scale landmark recognition engine. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009, 1085–1092.
• 24. Cox T., Cox M., Multidimensional Scaling, Second Edition, Chapman & Hall/CRC, 2000. 25. Cayton L. Algorithms for manifold learning, University of California, San Diego, Tech. Rep. CS2008–0923, 2005.
• 26. Tu Z. and Bai X. Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2010; 32(10):1744–1757.
• 27. von Luxburg U. A tutorial on spectral clustering. Statistics and Computing, 2007; 17(4):395– 416.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).