臺灣博碩士論文加值系統

本論文提出了基於內容的鳥類影像檢索系統(Content-based bird image retrieval system)。首先將輸入的鳥類影像做鳥類區域的偵測，去除因為背景而產生的不必要資訊，接著將偵測到的鳥類區域進行大小正規化，確保影像在進行特徵擷取與比對時，不會因為原始影像解析度的不同或拍攝距離遠近而產生的錯誤資訊，再來將影像進行色彩量化以減少顏色的複雜度。針對這些做完前處理的鳥類影像，為了解其顏色資訊，我們擷取了局部的色彩直方圖(Color Histogram)與色彩結構特徵(Color Structure)，接著為了解影像局部的紋理，我們擷取了尺度不變特徵轉換(Scale-invariant feature transform, SIFT)，然後將擷取到的局部特徵做詞彙的訓練去建立編碼簿(Codebook)做為統計詞袋(Bag of Words, BoW)特徵向量直方圖的依據，最後為了使特徵向量在空間上有位置的資訊，將觀察到鳥類常有的習性與前處理後影像的特性，在統計特徵直方圖時做了空間上的安排。而在檢索的部份，利用統計出的特徵直方圖在資料庫中找出與輸入影像相似度較相近的鳥類影像回饋給使用者。

In this paper, we will propose a content-based bird image retrieval system. It consists of two phases: feature extraction and bird retrieval. For bird region, the color histogram, color structure, and scale invariant feature transform(SIFT) are extracted as the features vector. Based on these feature vectors, the similar bird can be retrieved.

第一章 簡介 1
1.1 研究動機 1
1.2 相關文獻 2
第二章 鳥類影像檢索系統 5
2.1 前處理 6
2.1.1 鳥類區域偵測 6
2.1.2 鳥類區域大小正規化 10
2.1.3 色彩量化 10
2.2 特徵擷取 16
2.2.1 色彩直方圖特徵(Color Histogram) 17
2.2.2 色彩結構特徵(Color Structure) 18
2.2.3 尺度不變特徵轉換(Scale Invariant Feature Transform, SIFT) 19
2.2.4 建立編碼簿(Code Book) 20
2.2.5 統計Bag-of-Words(BoW)特徵直方圖 21
第三章 實驗結果 22
3.1 影像資料庫 22
3.2 檢索結果 24
第四章 結論 32
參考文獻 33

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Flickr: Find your inspiration.：https://www.flickr.com/

Instagram：https://www.instagram.com/

台灣地區野鳥集-鳥類- Natural Island, Yea! Taiwan：http://sjl.csie.chu.edu.tw/birds/index.php

Caltech-UCSD Birds-200-2011：http://www.vision.caltech.edu/visipedia/CUB-200-2011.html