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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2018 Vol.8(1): 54-60 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2018.8.1.663

Artwork Recognition for Panorama Images Based on Optimized ASIFT and Cubic Projection

Dayou Jiang and Jongweon Kim
Abstract—Few studies have been published on the object recognition for panorama images. To prevent the infringement of artworks in 360-degree images, we put forward an efficient method for artworks recognition inside 360-degree images in this paper. To start with, we employed the improved cubic projection to transform the distorted panorama image. Then, we used the optimized Affine Invariant Feature Transform (ASIFT) algorithm for extracting local features of transformed image. Finally, the feature point matching is based on one-to-one mapping constrain. The overall performance of the method is investigated on panorama dataset and the experimental results are compared with other well-known local feature extraction methods and original panorama image. The experimental results show that using the proposed method can improve around 30% of the accuracy for relatively higher distorted panorama images and reduce the computing time.

Index Terms—Panorama image, artwork, recognition, ASIFT, cubic projection.

Dayou Jiang is with Dept. of Copyright Protection, Sangmyung University, Seoul, Korea (e-mail: dyjiang@cclabs.kr).
Jongweon Kim is with Dept. of Electronics Engineering, Sangmyung University, Seoul, Korea (corresponding author; e-mail: jwkim@smu.ac.kr).

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Cite: Dayou Jiang and Jongweon Kim, "Artwork Recognition for Panorama Images Based on Optimized ASIFT and Cubic Projection," International Journal of Machine Learning and Computing vol. 8, no. 1, pp. 54-60, 2018.

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