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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/68746

    Title: 顯著物件與尺度不變特徵轉換特徵包比對之影像搜尋研究;The Study of Salient Object and BOF with SIFT for Image Retrieval
    Authors: 林政威;Lin,Cheng-Wei
    Contributors: 資訊管理學系
    Keywords: 影像檢索;基於內容之影像檢索;尺度不變特徵轉換;特徵包;K均數分群演算法;Image retrieval;Content-Based Image Retrieval;Scale Invariant Feature Transform;Bag of Features;K-means clustering algorithm
    Date: 2015-07-15
    Issue Date: 2015-09-23 14:23:01 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 有效地檢索數位影像,已成為影像檢索領域的重要研究。1990年,基於內容之影像檢索主要為擷取影像低階特徵;但是低階視覺特徵和高階語意概念之間仍存在著語意差距。本研究提出以尺度不變特徵轉換(Scale Invariant Feature Transform, SIFT)之特徵包(Bag of Features, BOF)模型結合影像之顯著物件概念的影像檢索系統,以物件圖像作為查詢影像標的之影像搜尋,透過影像含有的物件進行搜尋,並實作出影像搜尋系統。
    研究結果證實使用物件概念搜尋影像;並結合顯著物件與BOF與SIFT,確實比過去研究未結合顯著物件偵測之方法,較能夠提高影像搜尋準確率;最後,透過改良之系統搜尋方式與改善之影像搜尋準確率,實作出影像搜尋系統。;To effectively search digital images has become increasingly important in image retrieval (IR) area. In 1990’s, content-based image retrieval indexes images by their low-level features, but there are existing semantic gaps between low-level features and high-level semantic concepts. The study proposes an image retrieval system based on bag-of-features (BOF) with scale invariant feature transform (SIFT) combined salient object, to search through the objects contained in the image and to implement the real image retrieval system.
    This research detects a salient object in the image through salient object detection, and reduces the influence of background noise. After using salient object detection, SIFT features are extracted from each salient image in image database, and clustered using K-means clustering algorithm to form the codebook. SIFT features are extracted from object image, and found the nearest cluster center of the visual vector in codebook, and then the SIFT features of image are quantified using this visual vocabulary. Finally, an object image is presented as a set of visual words.
    In the experiments, image database is subset of image dataset MSRA-A. It contained 1000 images, which were equally divided into 10 different categories. The 1st experimental results showed that rectangle salient images perform better than original salient images in terms of salient object detection. The 2nd experiment studying the influence of the codebook size on retrieval performance of the system showed that the best size is 200 for this data set. The 3rd experimental results showed that using object concept is useful to find similar images that contain objects. From sensitivity analysis, providing a variety of query images through the transformation of object image can achieve better performance in image retrieval.
    In conclusion object images can improve the accuracy of image retrieval based on BOF with SIFT combined salient object. Eventually, the study is to implement an image retrieval system by changing the query method and improving the precision in image retrieval.
    Appears in Collections:[資訊管理研究所] 博碩士論文

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