| Course title | |||||
| 映像情報学特論 [Advanced Image Information Studies] | |||||
| Course category | courses for master's programs | Requirement | Credit | 2 | |
| Department | Year | ～ | Semester | 3rd | |
| Course type | 3rd | Course code | 1060611 | ||
| Instructor(s) | |||||
| 堀田 政二 [HOTTA Seiji] | |||||
| Facility affiliation | Faculty of Engineering | Office | Email address | ||
| Course description |
|
This course aims to introduce recent technics for understanding multimedia information (image, audio, video, and text) by a machine. We will examine popular machine learning techniques with some practice problems for understanding of these topics. Hence, prepare your laptop for every class. This course is designed to give an overview of various basic machine learning methods for understanding multimedia information. The course will give students an overall understanding of these fields and would make them realize that machine learning plays important roles in information technologies. The course will be based on lectures, discussions, and doing small projects (active learning style). |
| Expected Learning |
|
Learners who complete this course will be able to understand: - multimedia information, - applications for multimedia, - techniques for machine learning. Corresponding criteria in the Diploma Policy: See the Curriculum maps (Department of Computer and Information Sciences, diploma policy type A). |
| Course schedule |
|
1. Introduction schedule, importance of media understanding with machines, preparation 2. Paper selection I lecture for reading technical papers in English 3. Paper selection II explanation of technical terms 4. Paper selection III theme selection 5. History of object recognition implementation 6. Object detection and recognition I image understanding with computer vision, implementation 7. Object detection and recognition II similarity search of multimedia, implementation 8. Image categorization I feature vector, implementation 9. Image categorization II classification with machine learning, implementation 10. Image categorization II image understanding with contexts, experiments 11. Image segmentation clustering, experiments 12. Geometric invariance for image recognition experiments 13. Deep learning for image classification experiments 14. Conclusions summarization of advanced image information 15. Final presentation and report submission |
| Prerequisites |
| This lecture is part of the courses in the department of computer and information science. In addition to 30 hours that students spend in the class, and they are recommended to spend 60 hours for preparing and revising the lectures. |
| Required Text(s) and Materials |
| Prints are distributed in the lecture. |
| References |
| Papers selected by yourself. |
| Assessment/Grading |
| Report (50%) and Presentation (50%) |
| Message from instructor(s) |
| I want students to develop a deeper understanding of multimedia understanding with machines that will be a basic technology for future world. I would also like to encourage students to design their researches based on the knowledge given via this lecture. |
| Course keywords |
| image, video, segmentation, recognition, machine learning |
| Office hours |
| From 10:00 to 12:00 on Monday. Also, students can contact the chief instructor via e-mail. |
| Remarks 1 |
| Remarks 2 |
| Related URL |
| http://www.tuat.ac.jp/~s-hotta/IR/ |
| Lecture Language |
| Japanese |
| Language Subject |
| Last update |
| 4/6/2022 7:44:09 AM |