本文選題：圖像檢索 + 特征提取�。� 參考：《浙江理工大學》2017年碩士論文

【摘要】：信息化時代,生活中出現(xiàn)了海量的圖像信息。要從這些海量信息中檢索出與目標相似的圖像,一直是圖像檢索技術(shù)研究的目的。以前的圖像檢索技術(shù)主要基于文本。隨后,出現(xiàn)了基于圖像的顏色、紋理、形狀等來提取特征的算法,這些基于語義內(nèi)容上描述特征的檢索方法,即為基于內(nèi)容的圖像檢索技術(shù)(Content-Based Image Retrieval,CBIR)�；趦�(nèi)容的圖像檢索中的關(guān)鍵技術(shù)之一是特征提取技術(shù)。本文首先論述了課題的研究背景和現(xiàn)狀,并對基于內(nèi)容的圖像檢索技術(shù)作了結(jié)構(gòu)性介紹。然后,實驗分析了兩種傳統(tǒng)的特征提取技術(shù)在圖像檢索中的效果。最后,詳細介紹了三種發(fā)展較好的特征提取方法在圖像檢索上面的運用,并進行了實驗比較分析。本文主要工作如下:(1)在傳統(tǒng)的特征提取方法中,本文主要研究了CBIR中基于HSV空間顏色的特征提取方法和基于灰度共生矩陣的特征提取方法。并實驗分析了這兩種特征提取方法在圖像檢索方面的檢索效果。(2)研究了哈希算法在圖像檢索系統(tǒng)中的應(yīng)用。在哈希算法中,主要研究的是均值哈希算法的特征提取技術(shù),并通過離散余弦變換代替圖像尺寸縮小對其進行了改進。隨后,將改進后的均值哈希算法與改進前的均值哈希算法應(yīng)用于圖像檢索中,并對兩者的檢索效果進行了比較。實驗證明,改進后的均值哈希特征提取技術(shù)在CBIR中的檢索效果要優(yōu)于未改進的均值特征提取技術(shù)和兩種傳統(tǒng)的特征提取技術(shù)。(3)研究了SIFT算法在圖像檢索系統(tǒng)中的應(yīng)用。介紹了SIFT算法提取特征描述子的基本原理和具體步驟,并通過實驗分析了SIFT特征提取方法在CBIR中的表現(xiàn)。(4)研究了卷積神經(jīng)網(wǎng)絡(luò)在圖像檢索系統(tǒng)中的應(yīng)用。通過研究經(jīng)典的卷積神經(jīng)網(wǎng)絡(luò)模型,提出一種新型的預訓練卷積神經(jīng)網(wǎng)絡(luò)來提取圖像特征,并通過實驗分析了本文卷積神經(jīng)網(wǎng)絡(luò)模型在CBIR中的檢索效果。為了對各種特征提取方法的性能優(yōu)劣進行比較,本文中,各種算法都采用相同的實驗條件。實驗結(jié)果表明,與傳統(tǒng)的特征提取技術(shù)相比,新型的特征提取技術(shù)具有更好的檢索性能。
[Abstract]:In the information age, mass image information appears in the life. It is the aim of image retrieval technology to retrieve the image similar to the target from these massive information. Previous image retrieval techniques were mainly based on text. Subsequently, there are image based color, texture and shape algorithms to extract features. These semantic content-based feature retrieval methods are Content-Based Image Retrieval (CBIRN). Feature extraction is one of the key techniques in content-based image retrieval. In this paper, the background and present situation of the research are discussed, and the content-based image retrieval technology is introduced. Then, the effects of two traditional feature extraction techniques in image retrieval are analyzed. Finally, the application of three better feature extraction methods in image retrieval is introduced in detail, and the experimental results are compared and analyzed. The main work of this paper is as follows: (1) in the traditional feature extraction methods, this paper mainly studies the CBIR color extraction method based on HSV space and the feature extraction method based on gray level co-occurrence matrix. The effect of these two feature extraction methods in image retrieval is analyzed experimentally. The application of hashing algorithm in image retrieval system is studied. In the hash algorithm, the feature extraction technique of the mean hash algorithm is mainly studied, and the discrete cosine transform is used instead of the image size reduction to improve it. Then, the improved mean hash algorithm and the improved mean hash algorithm are applied to image retrieval, and the retrieval results are compared. Experimental results show that the improved mean hash feature extraction technique is better than the unimproved mean feature extraction technique and two traditional feature extraction techniques in CBIR.) the application of sift algorithm in image retrieval system is studied. This paper introduces the basic principle and concrete steps of feature descriptor extraction of sift algorithm, and analyzes the performance of sift feature extraction method in CBIR through experiments. The application of convolution neural network in image retrieval system is studied. By studying the classical convolution neural network model, a new pre-training convolution neural network is proposed to extract image features, and the retrieval effect of the convolution neural network model in CBIR is analyzed experimentally. In order to compare the performance of various feature extraction methods, the same experimental conditions are used in this paper. Experimental results show that compared with the traditional feature extraction technology, the new feature extraction technology has better retrieval performance.
【學位授予單位】：浙江理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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本文編號：2049768