DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Lee, Ju-Jang | - |
dc.contributor.advisor | 이주장 | - |
dc.contributor.author | Liong, Erin V. | - |
dc.contributor.author | Erin B. Liong | - |
dc.date.accessioned | 2013-09-12T02:00:57Z | - |
dc.date.available | 2013-09-12T02:00:57Z | - |
dc.date.issued | 2013 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=513385&flag=dissertation | - |
dc.identifier.uri | http://hdl.handle.net/10203/180963 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학과, 2013.2, [ vi, 40 p. ] | - |
dc.description.abstract | n Scene Recognition, one of the commonly used approach is using the Bag-of-Words(BoW) Model. The BoW approach basically collects local features from different classes/labels and cluster them together to create visual words that best represents all the features gathered. These visual words are used to represent each image through a co-occurence histogram matrix which is used as an observation model for training and testing the classifier. One popular extension done using BoW considers the spatial information of an image. Here, a Spatial Pyramid Matching scheme is used where the co-occurrence matrix are evaluated at every region of different resolutions/pyramid levels, and are cascaded together with fixed weights. This approach has shown great results, and has been considered as state-of-the-art. In this thesis, we improve Scene Recognition in two ways. First, is to improve codebook generation by creating a novel clustering algorithm which uses label information and an underlying assumption that less entropy clusters result to discriminative codebooks. Second, is having a novel implementation of Multiple Kernel Learning in Scene Recognition by using Spatial Kernels. Two approaches of MKL are investigated particularly Incremental-MKL and Generalized MKL. In Incremental MKL, an Adaboost framework is used where we choose the best pre-defined spatial kernels at every iteration based on the weighted data. Different types of kernel-based weak classifiers were investigated as well, particularly, weakSVM and Dyadic Hypercut. In Generalized MKL, we deal with a non-convex optimization problem which is formed from extending the SVM problem and adding a regularization term. Here, $\ell1$ (sparse) and $\ell2$ (euclidean norm) cases are investigated and optimized. The proposed approach is tested and evaluated using benchmark datasets and is compared with state-of-the-art algorithms. Results shown that Incremental MKL using weak-SVM (SPBoost-SVM) and Generalized M... | eng |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Scene Recognition | - |
dc.subject | Bag-of-Words | - |
dc.subject | Spatial Pyramid Matching | - |
dc.subject | 배경 인식 | - |
dc.subject | 단어주머니 | - |
dc.subject | 국지적 피라미드 매칭 | - |
dc.subject | 다중 커널 학습 | - |
dc.subject | Multiple Kernel Learning | - |
dc.title | Scene recognition using improved codebook generation and multiple kernel learning | - |
dc.title.alternative | 향상된 코드북 생성과 다중 커널 학습을 이용한 배경인식 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 513385/325007 | - |
dc.description.department | 한국과학기술원 : 전기및전자공학과, | - |
dc.identifier.uid | 020114275 | - |
dc.contributor.localauthor | Lee, Ju-Jang | - |
dc.contributor.localauthor | 이주장 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.