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利用基因演算法進行紋理特徵編碼最佳化及其在影像分群之應用 = Genet...

國立高雄大學電機工程學系碩士班

利用基因演算法進行紋理特徵編碼最佳化及其在影像分群之應用 = Genetic-based optimal encoding for image clustering with texture-based features

紀錄類型:	書目-語言資料,印刷品 : 單行本
並列題名:	Genetic-based optimal encoding for image clustering with texture-based features
作者:	盧立偉,
其他團體作者:	國立高雄大學
出版地:	高雄市
出版者:	國立高雄大學;
出版年:	2013[民102]
面頁冊數:	81葉部分彩圖，表格 : 30公分;
標題:	影像分群
標題:	image clustering
電子資源:	https://hdl.handle.net/11296/62t85j
附註:	107年11月1日公開
附註:	參考書目：葉77-81
摘要註:	影像分群的目的是對以特徵(feature)描述的影像像素進行分割，將具同質性特徵的像素群聚成同一集合。特徵同質程度的計算，通常是以特徵間的歐幾里德距離(Euclidean distance)作為衡量標準。因此以距離為依據的分群方式，對具有連續變化的影像特徵，像是顏色、亮度、飽和度等，皆能獲得良好的效果。紋理(texture)也常常作為分群的特徵依據，以往的研究通常直接將紋理以固定形式編碼，造成紋理編碼的歐幾里德距離變化量不具連續的一致性，造成分群結果的不穩定。本論文提出一套以基因演算法(genetic algorithm, GA)為基礎的編碼方式，來處理上述問題中，並以區域二元圖樣(local binary pattern)為例，說明其在影像分群的影響。本研究中以兩階段的GA實現LBP的最佳化編碼，使LBP式樣間相似的式樣具相對較短的距離；反之亦然。第一階段GA安排所有的 LBP的順序，使全部圖樣在排列上呈現連續變化；第二階段GA分配每個圖樣一個唯一的整數編號，讓相似(相異)的圖樣在歐幾里德距離衡量上，具有較短(長)的距離。本論文以 Fuzzy C-means作為分群方式，並比較多種不同的編碼方式。藉由多張影像測試與實驗結果分析得知，本方法可以找出一組可行的編碼，並提升了以 LBP紋理為特徵的影像分群品質。 For image clustering, homogeneous and meaningful image pixels with specific features are clustered.The homogeneity of various features is usually calculated by the Euclidean distances among features.For features that have continuous variations, such as color, luminance, saturation, gradient of intensity, distance-based clustering can give effective results. When textures are used as features for clustering, an encoding scheme that describes the variations of textures in terms of distances can produce effective clustering results. This study proposes a genetic-based encoding method to deal with the abovementioned problem where the local binary pattern (LBP) is employed as the texture for clustering. The genetic algorithm (GA) is used to implement the optimal encoding scheme of LBP-based textures. In the encoding scheme, similar LBP-textures are required to have shorter distances, and vice versa. The GA process is separated into two stages. The first stage arranges the locations for all LBP patterns so that they can have continuous variations. The second stage assigns each LBP pattern a unique integer in a manner that similar (dissimilar) patterns have short (long) distances in the same Euclidean scale. A fitness function describing these requirements is defined.In this study, fuzzy c-means is used as the clustering method. Various encoding methods are compared with the proposed method. From the experimental results, the genetic-based encoding method finds a feasible set of encodes for LBP-based textures and improves the quality of image clustering. Some images are tested and the results are analyzed.

利用基因演算法進行紋理特徵編碼最佳化及其在影像分群之應用 = Genetic-based optimal encoding for image clustering with texture-based features
盧, 立偉

利用基因演算法進行紋理特徵編碼最佳化及其在影像分群之應用 = Genetic-based optimal encoding for image clustering with texture-based features / 盧立偉撰 - 高雄市 : 國立高雄大學, 2013[民102]. - 81葉 ; 部分彩圖，表格 ; 30公分.
107年11月1日公開參考書目：葉77-81.
影像分群image clustering

利用基因演算法進行紋理特徵編碼最佳化及其在影像分群之應用 = Genetic-based optimal encoding for image clustering with texture-based features
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