本文選題：稀疏編碼 + 分層學習　； 參考：《西安電子科技大學》2014年碩士論文

【摘要】：在如今多媒體信息技術(shù)迅速發(fā)展的時代,數(shù)字圖像越來越多,如何在海量的圖像中迅速查找到用戶感興趣的圖像或者迅速將圖像分門別類便于后續(xù)的處理是一個很緊迫的任務。圖像場景分類是根據(jù)圖像內(nèi)容自動獲取圖像所屬類別的一種技術(shù),已經(jīng)在模式識別、計算機視覺等領域中得到廣泛應用。遙感圖像的場景分類作為圖像場景分類的一個重要分支,近年來已經(jīng)對遙感圖像的目標檢測、圖像檢索、圖像增強等實際問題的研究做出了很大貢獻。遙感圖像場景分類首先需要從圖像中提取特征,然后選擇合適的分類器進行分類。所以圖像的特征提取很關(guān)鍵,對于分辨度很低的特征,再好的分類器也會失效。遙感場景分類的方法主要分為基于底層特征的方法和基于中層特征的方法兩類�；诘讓犹卣鞯姆椒ú恍枰R別圖像場景中具體的景物,所以相對而言計算復雜度比較低,但是對于圖像中比較復雜的場景,底層特征的分類效果很差。這就是底層特征和高層語義之前存在的鴻溝。為解決這種鴻溝,提出了基于中層特征的方法,在底層特征和高層特征之間搭建了橋梁。本文針對圖像場景分類主要有以下幾個工作:1.介紹了基于多路分層正交匹配追蹤(Orthogonal Matching Pursuit,OMP)的半監(jiān)督遙感圖像場景分類方法。不同于傳統(tǒng)的基于局部特征描述子的方法,本方法是直接從原始圖像塊出發(fā)學習字典,運用正交匹配追蹤的稀疏編碼方法、金字塔模型(Spatial Pyramid Matching,SPM)得到整幅圖像的特征表示,并結(jié)合多路分層學習思想和最大池化方法構(gòu)建了基于不同大小的圖像塊的無監(jiān)督特征學習框架,最后采用半監(jiān)督的支持矢量機(Semi-Supervised Support Vector Machine,S3VM)進行分類。并將此分類方法擴展到遙感圖像的場景檢測當中。實驗結(jié)果表明此算法在遙感圖像的場景分類和檢測上都能夠取得不錯的效果。2.提出了基于局部特征描述子和分層稀疏編碼的遙感圖像場景分類方法。該方法改變傳統(tǒng)特征包模型單尺度單層的學習模式,在多個尺度的局部圖像塊上提取SIFT(Scale invariant feature transform)和LBP(Local Binary Patterns)局部特征描述子,并根據(jù)不同尺度的局部特征進行分層稀疏編碼,最后將不同尺度下學習的圖像特征聯(lián)合,再用SVM(Support Vector Machine)進行分類。該算法相對傳統(tǒng)的基于SIFT和LBP特征的場景分類方法,在遙感圖像場景分類中正確率提高了很多。3.提出了基于多路分層正交匹配追蹤的半監(jiān)督圖像場景分類方法的MATLAB(MATrix LAboratory)多核并行加速算法。原算法中對圖像密集采樣、編碼、池化等操作均是相同算法對不同數(shù)據(jù)的獨立處理過程,實驗的大數(shù)據(jù)量給參數(shù)優(yōu)化造成很大的困難。本文運用MATLAB多核并行平臺將這些計算過程相互獨立的算法設計為并行結(jié)構(gòu)。對比實驗證明該并行算法大大降低了時間復雜度,解決了優(yōu)化參數(shù)中的難題。
[Abstract]:Nowadays, with the rapid development of multimedia information technology, more and more digital images are available. It is an urgent task how to quickly find the images of interest to the users in the massive images or to classify the images quickly to facilitate the subsequent processing. Image scene classification is a kind of technology which can automatically obtain the category of image according to the content of image. It has been widely used in the fields of pattern recognition computer vision and so on. As an important branch of image scene classification, scene classification of remote sensing images has made great contributions to the research of target detection, image retrieval and image enhancement in recent years. The scene classification of remote sensing images needs to extract features from the image and then select the appropriate classifier for classification. So the feature extraction of image is very important, and for the feature with low resolution, the better classifier will fail. The methods of remote sensing scene classification are divided into two categories: the method based on the bottom feature and the method based on the middle feature. The method based on the underlying feature does not need to recognize the specific scene in the image scene, so the computational complexity is relatively low, but for the more complex scene in the image, the classification effect of the underlying feature is very poor. This is the gap between underlying features and high-level semantics. In order to solve this gap, a method based on middle level feature is proposed, which builds a bridge between the bottom feature and the high level feature. In this paper, the image scene classification has the following work: 1. A semi-supervised remote sensing image scene classification method based on multi-channel hierarchical orthogonal matching tracking orthogonal matching pursuit (OMP) is introduced. Different from the traditional method based on local feature descriptors, this method is to learn the dictionary directly from the original image block, using the sparse coding method of orthogonal matching tracing, and the pyramid model is used to obtain the feature representation of the whole image. The unsupervised feature learning framework based on different size image blocks is constructed based on the idea of multi-path hierarchical learning and the maximum pool method. Finally, semi-supervised support vector machine Semi-Supervised support Vector Machine (S3VM) is used for classification. The classification method is extended to the scene detection of remote sensing images. Experimental results show that the algorithm can achieve good results in scene classification and detection of remote sensing images. A method of remote sensing image scene classification based on local feature descriptor and hierarchical sparse coding is proposed. This method changes the learning mode of single scale and single layer of traditional feature packet model, extracts local feature descriptors of sift scale invariant feature transform) and LBP local binary patterns on local image blocks of multiple scales, and performs layered sparse coding according to local features of different scales. Finally, the image features of different scales are combined and classified by SVM support Vector Machine. Compared with the traditional scene classification method based on sift and LBP, the algorithm improves the accuracy of scene classification in remote sensing images by a lot of .3. A multi-core parallel acceleration algorithm for semi-supervised image scene classification based on multi-channel hierarchical orthogonal matching tracking is proposed in this paper. In the original algorithm, the operations such as dense image sampling, coding and pool processing are all independent processing processes of different data by the same algorithm, and the large amount of experimental data makes parameter optimization very difficult. In this paper, MATLAB multi-core parallel platform is used to design these algorithms which are independent of each other as parallel structure. The comparison experiments show that the parallel algorithm greatly reduces the time complexity and solves the problem of optimizing parameters.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP751

【參考文獻】

相關(guān)碩士學位論文 前1條

1 張雪;基于特征學習的圖像場景分類[D];西安電子科技大學;2014年

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本文編號：2027479