- 學位論文
應用畫面分割技巧於場景檢測與影像檢索之方法
Application of Frame Partition Scheme to Shot Detection and Image Retrieval
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摘要
本論文以分割畫面技巧(Frame Partitioning Scheme)為基礎，提出場景檢測與影像檢索方法。在場景檢測方面，基於前後分割畫面差之場景轉換檢測方法，稱為SD/PFDS (Shot Detection Based on Partitioned Frame Differencing Scheme)。在SD/PFDS方法中，我們以畫面群為基本單位，以第一張畫面為參考畫面，後續畫面為比較畫面，並將每一張畫面做分割，計算分割畫面間的像素差值來判斷是否發生場景轉換。在實驗範例中，我們所提的方法在大多數影片類型當中，不管是突然式場景變換、漸進式場景變換之檢測正確率都有不錯的表現，這說明了所提的SD/PFDS方法是具體可行的。 另外，我們也應用分割畫面的技巧於影像內容檢索CBIR (Content-Based Image Retrieval)。基於色彩和紋理特徵，本論文提出了IR/PCF (Image Retrieval with Partitioned Color Features)、IR/PTF (Image Retrieval with Partitioned Texture Features)與IR/PCTF(Image Retrieval with Partitioned Color and Texture Features)等檢索方法。在IR/PCF方法中，以分割顏色特徵檢索的步驟如下：首先，將影像分割為數個區塊。第二、基於查詢影像中R、G、B三個成分的能量(Energy)找出各成分之權重，用於相似度評估計算。第三、在分割影像區塊中，計算各成分平均值做為顏色特徵。第四，利用權重計算相似度，得到顏色相似度距離。 在IR/PTF方法中，我們使用灰階共生矩陣GLCM (Gray Level Co-occurrence Matrix)來擷取紋理特徵，其檢索步驟如下：首先，將彩色影像轉為灰階影像並分割為數個區塊。第二、計算各分割影像區塊中的GLCM紋理特徵。第三、計算查詢影像與資料庫影像之紋理特徵距離。 最後，我們結合顏色與紋理二個特徵於影像檢索中，在IR/PCTF方法中，顏色與紋理特徵的距離分別經過正規化，然後依照特徵的重要性取權重，得到相似度距離。實驗結果顯示，在所提的三個方法中，IR/PCTF方法具有最好的檢索，其次是IR/PCF方法，最後是IR/PTF方法。這說明了適當的結合顏色與紋理特徵，確實比起使用單一特徵有更好的檢索效能，並且能明顯提升檢索效能。
並列摘要
This thesis presents approaches to shot detection and image retrieval based on frame partitioning scheme. For the shot detection, the proposed approach is called SD/PFDS (Shot Detection Based on Partitioned Frame Differencing Scheme). In the SD/PFDS, frames are grouped and partitioned into image blocks. The first frame in the group is considered as reference frame and the others compared frames. Then the differences for each image blocks between partitioned reference and compared frames are calculated. By the differences, changes of shots are detected. The proposed SD/PFDS approach is verified by several examples. The results indicate that the overall average accuracy of detection is as high as 0.94 in F1 measure. By the results, the SD/PFDS approach has been justified and shown feasible. Also, we apply the frame partitioning scheme to image retrieval. With color and texture features, the thesis present three approaches to image retrieval: IR/PCF (Image Retrieval with Partitioned Color Features), IR/PTF (Image Retrieval with Partitioned Texture Features), and IR/PCTF (Image Retrieval with Partitioned Color and Texture Features). Based on partitioned color features, several stages are involved in the IR/PCF. First, images are partitioned. Second, energies in R-, G-, B-components for the partitioned query image are calculated through which weights on the similarity measure are found. Third, find averages of R-, G-, B-components in partitioned image as color features. Finally, calculate the similarity with weights obtained in the second stage. In the IR/PTF, texture features are acquired by GLCM (Gray Level Co-occurrence Matrix). The IR/PTF approach consists of the following stages. First, convert color images in gray-level images. Second, find texture features by GLCM in the partitioned images. Third, calculate the similarity between query image and images in database. The IR/PCTF approach uses both partitioned color and texture features. The following stages are involved in the IR/PCTF. First, the similarity measures are obtained by the IR/PCF and the IR/PTF, respectively. Then the similarity measures are normalized and linearly combined with weights proportional to the performance with only one partitioned feature, i.e., color or texture. The resulted similarity is then used in image retrieval. The three proposed image retrieval approaches are justified by image databases. It shows that the IR/PCTF is of highest retrieval performance and then the IR/PCF, and finally the IR/PCTF. With an appropriate combination of partitioned color and texture features, the IR/PCTF shows better performance than those in the IR/PCF and the IR/PTF.
參考文獻
[1] Hun Cheol Lee, Cheong Woo Lee, Seong Dae Kim, “Abrupt Shot Change Detection Using an Unsupervised Clustering of Multiple Features,” IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, pp. 2015-2018, Vol. 4, 2000.
[2] Alan Hanjalic, Hong-Jiang Zhang, “An Integrated Scheme for Automated Video Abstraction Based on Unsupervised Cluster Validity Analysis,” IEEE Trans. Circuits Sys. Video Tech., Vol. 9, pp. 1280-1289, Dec. 1999.
[3] Jianyu Wang, Wen Luo, “A Self-adapting Dual-threshold Method for Video Shot Transition Detection,” IEEE Int. Conf. on Networking, Sensing and Control. pp. 704-707, Apr. 2008.
[4] Waqas Rasheed, Gwangwon Kang, Jinsuk Kang, Jonghun Chun, Jongan Park, “Sum of Values of Local Histograms for Image Retrieval,” IEEE Int. Conf. on Networked Computing and Advanced Information Management, Vol. 2, pp. 690-694, Sept. 2008.
[5] Waqas Rasheed, Youngeun An, Sungbum Pan, Ilhoe Jeong, Jongan Park, Jinsuk Kang, “Image Retrieval Using Maximum Frequency of Local Histogram Based Color Correlogram,” IEEE Int. Conf. on Modeling & Simulation, pp. 322-326, May 2008.