臺灣博碩士論文加值系統

以內容為基礎的影像檢索依照與人眼的觀感符合之影像特徵來對影像資料庫進行檢索。本文中提出了一個新的影像特徵，稱之為頻率層特徵，利用人眼對於不同的頻率會有不同的處理，而得到不同頻率層的形狀及顏色特徵。
有鑑於每個使用者對於影像會有其主觀性，對於不同的使用者的觀感而言，各種特徵的重要性是不一致的。本文提出了用二種不同方法來進行使用者回饋以解決者種使用者的主觀性問題。其一是基於類神經網路技術，另一則以最大可能性估測法來設計。根據實驗的結果顯示，我們所提出的二種方法都相當有效，而且，以數個影像資料庫測試的比較結果顯示，最大可能性估測法較類神經網路有更為理想的使用者回饋效果。證實我們的方法即使對於不同的影像特徵都可以藉由這樣的調整改善檢索的效果。

Content-Based Image Retrieval (CBIR) takes visual features, instead of textual annotations, of images as the keys to retrieval images. This thesis presents a novel feature called “frequency layer” that reveals both the color and shape of image contents with respect to different frequencies of textures from the perspective of human vision.
As every user may have his own subjective perception on the same image contents, each extracted key of visual feature should be of different importance for different users. This thesis also proposes two methods of relevance feedback to handle this problem of users’ subjectivity. One is designed on a neural network (NN) approach, while the other is based on the technique of maximum likelihood estimation (MLE). In to the experimental results in this research work, the performance of both methods are evaluated and compared. The results on testing some image databases show that both methods are effective. Moreover, the MLE method outperforms the NN approach.

中文摘要 III
Abstract IV
致謝 (Acknowledgement) V
第1章 緒論1
1.1 研究背景與動機1
1.2 系統架構2
第2章 相關研究4
2.1 影像檢索技術4
2.2 相關性回饋技術6
第3章 影像特徵抽取8
3.1 影像頻率分頻8
3.2 顏色量化（Color Quantization）10
3.3 動量特徵（Moment）11
3.4 主成份分析（Principal Component Analysis）12
第4章 使用者回饋方法14
4.1 類神經網路相關性回饋14
4.2 最大可能性估測法（Maximum Likelihood Estimation）之相關性回饋16
第5章 實驗結果20
5.1 類神經網路之相關性回饋實驗23
5.2最大可能性估測法之相關性回饋實驗30
5.3 結果比較與討論35
第6章 總結39
6.1 結論39
6.2 未來研究方向39
參考文獻 40

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