近年來，很多要求高安全性的網站都使用扭曲變形的英文或數字字串作為辨識碼，以避免網站或系統受到大量暴力的攻擊。微軟公司則於2007年提出以貓狗影像的新辨識碼系統—Asirra。對於電腦而言，貓狗影像辨識較字串更為困難。本研究主要針對Asirra的影像資料試圖建構出貓狗影像自動辨識法，藉此來了解此辨識碼系統的有效性。已知影像包含大量雜訊，若使用原始資料則計算困難而且辨識效果差，所以萃取關鍵特徵為重要的研究課題。本文考慮方向梯度直方圖法 (Histograms of Oriented Gradients, HOG) 以及主成分分析 (Principal Components Analysis, PCA) 來篩選重要變數。我們將運用挑選出的特徵建立支持向量機 (Support Vector Machine, SVM) 分類器。在實證分析中，我們發現結合此兩種特徵萃取法，除了能夠大幅降低運算時間，也能得到良好的預測正確率。 / In recent years, many websites, which requires a high standard of security, use CAPTCHA to avoid mass and brutal attacks from hackers. The CAPTCHA considers the use of strings of twisted and deformed English letters or numbers as an identification code. In 2007, the company Microsoft proposed a new image-based recognition system-Assira, which uses dogs and cats images as an identification code. Dogs and cats image recognition is not more difficult than strings of letters or numbers recognition for human, but is more challenging for computers. In this paper, we aim to develop a classification method for images from Asirra. An image is represented by an enormous number of pixels. Only few pixels carry important feature information, most pixels are noise. The abundance of noise leads to computational inefficiency, and even worse, may results in inaccurate recognition. Therefore, in this problem feature extraction is an essential step before a classifier construction. We consider HOG (Histograms of Oriented Gradients) and PCA (Principal Components Analysis) to select important features, and use the features to construct a SVM (Support Vector Machine) classifier. In the real example, we find that combining the two feature detection methods can dramatically reduce computational time and have satisfactory predictive accuracy.

影像辨識一直是電腦視覺中很重要的技術，且伴隨著行動裝置與相機的普及，人們更加重視辨識的準確度與效能，以區域梯度分佈及直方圖表示方法為基礎的影像特徵描述子，如SIFT與SURF，是近十多年來的物件辨識技術中所採用的主流演算法，然而此類特徵表示法，常需要為多維度的資訊提供大量的儲存空間與複雜的距離計算流程，因此，近年來有學者提出了另一種形式的區域二元特徵描述子 ( Local Binary Descriptor, LBD)，以二元架構建立描述子，使得LBD能在較少空間之下提供可相抗衡的辨識率。 本論文提出以多元編碼機制之區域特徵描述子(LMLED)，乃基於LBD的基本架構，但改以多元編碼取代LBD的二元編碼方法，利用緩衝區的架構達到更強的抗噪性，並提出降維方法以承襲二元編碼在儲存空間的優勢，使得多元編碼機制之區域特徵描述子能在不影響匹配能力與儲存空間的情況下，得到更佳的影像辨識能力。 / Efficient and robust object recognition is an important yet challenging task in computer vision. With the popularity of mobile equipment and digital camera, the demand for effectiveness and efficiency in image recognition has become increasingly pressing. In the past decade, local feature descriptors based on the distribution of local gradients and histogram representation such as SIFT and SURF have achieved a certain level of success. However, these descriptors require a large amount of storage and computing resources for high dimensional feature vectors. Hence, local binary descriptor (LBD) arises and becomes popular in recent years, providing comparable performance with binary structure that needs dramatically lower storage cost. In this thesis, we propose to employ multi-level encoding scheme to replace binary encoding of LBD. The resultant descriptor is named local multi-level encoding descriptor (LMLED). LMLED takes advantage of multiple decision intervals and thus can achieve better noise resistivity. Methods to reduce the dimension have been devised to maintain low storage cost. Extensive experiments have been performed and the results validate that LMLED can achieve superior performance under noisy condition while maintaining comparable matching efficacy and storage requirement.