Content-based image analysis with applications to the multifunction printer imaging pipeline and image databases

<p>Image understanding is one of the most important topics for various applications. Most of image understanding studies focus on content-based approach while some others also rely on meta data of images. Image understanding includes several sub-topics such as classification, segmentation, retrieval and automatic annotation etc., which are heavily studied recently. This thesis proposes several new methods and algorithms for image classification, retrieval and automatic tag generation. The proposed algorithms have been tested and verified in multiple platforms. For image classification, our proposed method can complete classification in real-time under hardware constraints of all-in-one printer and adaptively improve itself by online learning. Another image understanding engine includes both classification and image quality analysis is designed to solve the optimal compression problem of printing system. Our proposed image retrieval algorithm can be applied to either PC or mobile device to improve the hybrid learning experience. We also develop a new matrix factorization algorithm to better recover the image meta data (tag). The proposed algorithm outperforms other existing matrix factorization methods.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Engineering

Year

2016

Author dc:creator

• Lu, Cheng

Contributors dc:contributor

• Jan P. Allebach
• Mary L. Comer
• Edward J. Delp
• Yung-Hsiang Lu

• Applied sciences
• Classification
• Content-based
• Database
• Image anaylsis
• Multifunction printer
• Computer Engineering

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/673

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-1809