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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/49315

Title: 基於機率潛在語意分析與稀疏表示之車輛顏色分類技術
PLSA-based Sparse Representation for Vehicle Color Classification
Authors: Wang, Ssu-Ying
王思穎
Contributors: NTOU:Department of Computer Science and Engineering
國立臺灣海洋大學:資訊工程學系
Keywords: 稀疏表示;機率潛在語意分析;物件分類
Sparse Representation;Probabilistic Latent Semantic Analysis (pLSA);Object Classification
Date: 2015
Issue Date: 2018-08-22T06:56:34Z
Abstract: 物件分類在影像處理上為非常重要的研究領域之一,在許多論文上面都有提出許多方法來辨識分類物件,例:稀疏表示分類法 (Sparse Representation Classification, SRC)。在這些研究中大多是藉由稀疏表示法對物件做字典學習來建立其字典後用重建訊號的殘餘值之資訊來對目標物件做辨識分類。而因為只使用重建訊號殘餘值(Residual of Reconstruction Error)之資訊並未考慮其Visual Codes的線性組合分佈,在各分類的極度相似的狀況下容易分類錯誤,並且因為在計算重建訊號的殘餘值時多使用正交匹配追蹤演算法(Orthogonal Matching Pursuit, OMP)來求解最佳化問題,而這需要花費長時間在求解最佳化問題上。 因此我們提出一種新的分類方法將機率潛在語意分析(Probabilistic Latent Semantic Analysis, pLSA)結合稀疏表示,藉由pLSA的主題模型概念找出其潛在類別來分析物件,以此解決以往使用重建訊號的殘餘值來分類時所造成的分類錯誤。 我們透過車輛顏色分類的實驗結果來顯示出本論文所提出的方法相較於以往SRC方法更為準確且效率更佳。
Object classification is an important research of the image processing. In many papers, they use many ways to classify the objects, like Sparse Representation Classification (SRC). SRC needs to calculate the residual of reconstruction error and to find the best candidate. It is very inefficient because of an optimization process is involved. In addition, it uses only the residual that ignore to consider the distribution of combination coefficients of visual codes in classification. Thus, it often fails to classify categories when they are similar. In this thesis, we proposed a novel classification method that combined probabilistic latent semantic analysis (pLSA) and sparse representation. We use the results of vehicle color classification experimental to prove our proposed method is to improve the SRC.
URI: http://ethesys.lib.ntou.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=G0010257033.id
http://ntour.ntou.edu.tw:8080/ir/handle/987654321/49315
Appears in Collections:[資訊工程學系] 博碩士論文

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