2026-09-21T04:31:14Zhttps://digital.library.adelaide.edu.au/server/oai/request

oai:digital.library.adelaide.edu.au:2440/1125882026-06-12T08:31:07Zcom_2440_14759col_2440_14760

Shen, Chunhua Jiang, Yuchao School of Computer Science 2017 http://hdl.handle.net/2440/112588 10.4225/55/5b10b6edb84c6 Sketch is a special group of images, and the ability to recognize sketches is of great importance for many applications, including the human-computer interaction and childhood education. Previous approaches often pose this as a sub-class of general image classification which can be solved with a conventional pattern recognition method. In this thesis, instead of applying general image classification methods directly to sketches, a model using a new deep neural network that considers more of the unique characteristics of sketches has been developed and studied. Experiment results on the challenging sketch datasets demonstrate the superior performance of this present model in comparison to previous state-of-the-art methods. In addition to a novel method for sketch image classification, the topic has been further expanded to enable the matching of face sketches to photo images. This is of critical significance, especially in the law enforcement area to identify criminals. Previous work has attempted to address this task by exploring invariant features or looking for a shared subspace. In this paper, an end-to-end method has been proposed whereby the similarity score can be obtained directly when inputting a pair of sketch and photo-face images. In particular, this study investigates matching image pairs with more diversity than previous studies, considering such features as the presence of a beard, or haircut style, etc. The approach taken in this study is one utilizing a CNN based model, which is more robust and applicable to the complexities existing in the real world. The method presented exhibits good performance with both forensic sketch dataset and viewed sketches. Furthermore, in this study, the largest face sketch dataset has been created which can accommodate, in total, 14750 positive pairs and 985512 negative pairs for evaluation, which will facilitate future research. Thesis (M.Phil.) -- University of Adelaide, School of Computer Science, 2017. sketch CNN classification face recognition Sketch image recognition using deep features Theses This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legalsopen.access