面向檢索的多視覺特征融合
發(fā)布時間：2018-11-05 09:32
【摘要】：近幾十年來,伴隨著互聯網的快速發(fā)展以及智能終端的普及,互聯網上數字圖像的數量呈現爆炸式增長。面對海量的圖像信息,如何快速高效地檢索這些圖像一直是學術界和工業(yè)界研究的熱點課題。圖像的特征表達是基于內容的圖像檢索的最基本問題之一。為了提升檢索的準確度,研究人員從顏色、紋理等不同的角度提出不同的視覺特征表達來表征圖像。選擇不同的視覺特征對于圖像檢索的準確度有很大的影響。一般來說,采用具有一定互補性的多種特征進行融合是提升圖像檢索準確度的一種方法。為了把基于不同特征得到的圖像檢索結果融合在一起,我們有兩個關鍵的問題需要解決。第一個關鍵問題是如何使基于不同特征空間的距離度量是可比擬的。因為通常使用不同的特征,如SIFT,HSV,CNN特征,算得的距離是不在一個尺度空間的。直接把不在一個尺度空間的"距離"進行相加是不合適的。第二個需要關注的關鍵的問題是,如何自適應的度量不同的特征的有效性。因為對于某些查詢圖像來說,局部特征就能取得較好的檢索結果。然而對于另外某些查詢圖像,用全局特征比如CNN特征才能夠得到比較好的檢索結果。對于同一個查詢圖像,我們需要比較并量化不同特征的有效性。基于上面的兩個關鍵點,我們的工作主要歸納如下:(1)基于圖模型的自適應加權特征融合方法。在此方法中,圖模型把本來在不同尺度空間的距離度量,都統(tǒng)一到一個Graph里面,并用統(tǒng)一的度量方法Jaccard系數來度量各個圖片之間的相似度。同時為了衡量不同特征的有效性,我們使用PageRank算法對不同特征構建的圖進行分析,并根據最后得到的PageRank值的分布來衡量不同特征的有效性。最后根據特征對特定檢索圖像的有效性,完成不同特征構建的圖的自適應加權融合。根據最后融合得到的圖,我們解出最后的圖片檢索排序。(2)基于鄰域相似度分布的自適應多特征融合方法。該方法是根據圖像在給定的視覺特征下的近鄰空間的分布情況,來進行特征融合。不同特征對于一個具體的查詢圖像得到的k近鄰的距離空間分布是不一樣的。我們通過探索k近鄰的空間分布特性,來進行衡量不同特征的有效性。我們提出了有效性系數的概念-REC(Rank Effectiveness Coefficient)。REC 反映 了一個特征對一個具體圖像的有效性。通過有效性系數對原來特征的相似度進行加權融合,最后得到融合后的相似度得分。根據融合后的相似度得分,可以給出最后的圖像檢索排序結果。
[Abstract]:In recent decades, with the rapid development of the Internet and the popularity of intelligent terminals, the number of digital images on the Internet has increased explosively. In the face of massive image information, how to retrieve these images quickly and efficiently has been a hot topic in academia and industry. Image feature representation is one of the most basic problems in content-based image retrieval. In order to improve the retrieval accuracy, the researchers proposed different visual features to represent the image from different angles such as color and texture. The selection of different visual features has great influence on the accuracy of image retrieval. Generally speaking, it is a method to improve the accuracy of image retrieval by using a variety of complementary features. In order to fuse the image retrieval results based on different features, we have two key problems to be solved. The first key problem is how to make distance metrics based on different feature spaces comparable. Because different features, such as SIFT,HSV,CNN features, are usually used, the distance calculated is not in the same scale space. It is not appropriate to add "distances" that are not in a scale space directly. The second key concern is how to measure the effectiveness of different features adaptively. Because for some query images, local features can obtain better retrieval results. However, for some other queried images, global features such as CNN features are used to obtain better retrieval results. For the same query image, we need to compare and quantify the validity of different features. Based on the above two key points, our work is summarized as follows: (1) Adaptive weighted feature fusion method based on graph model. In this method, the graph model unifies the distance measure in different scale space into one Graph, and uses the unified measure method Jaccard coefficient to measure the similarity of each picture. At the same time, in order to evaluate the validity of different features, we use PageRank algorithm to analyze the graph constructed by different features, and evaluate the validity of different features according to the distribution of PageRank values. Finally, according to the validity of the feature to the specific retrieval image, the adaptive weighted fusion of the graph constructed by different features is completed. According to the final fusion graph, we solve the final image retrieval ranking. (2) Adaptive multi-feature fusion method based on neighborhood similarity distribution. This method is based on the distribution of the image in the nearest neighbor space under the given visual features. The distance distribution of k-nearest neighbor is different for a specific query image with different features. We evaluate the effectiveness of different features by exploring the spatial distribution of k-nearest neighbors. We propose the concept of validity coefficient-REC (Rank Effectiveness Coefficient). REC reflects the validity of a feature to a specific image. The similarity of the original feature is weighted by the validity coefficient, and the score of the similarity after the fusion is obtained. According to the similarity score of fusion, the final result of image retrieval and sorting can be given.
【學位授予單位】：中國科學技術大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
本文編號：2311654
[Abstract]:In recent decades, with the rapid development of the Internet and the popularity of intelligent terminals, the number of digital images on the Internet has increased explosively. In the face of massive image information, how to retrieve these images quickly and efficiently has been a hot topic in academia and industry. Image feature representation is one of the most basic problems in content-based image retrieval. In order to improve the retrieval accuracy, the researchers proposed different visual features to represent the image from different angles such as color and texture. The selection of different visual features has great influence on the accuracy of image retrieval. Generally speaking, it is a method to improve the accuracy of image retrieval by using a variety of complementary features. In order to fuse the image retrieval results based on different features, we have two key problems to be solved. The first key problem is how to make distance metrics based on different feature spaces comparable. Because different features, such as SIFT,HSV,CNN features, are usually used, the distance calculated is not in the same scale space. It is not appropriate to add "distances" that are not in a scale space directly. The second key concern is how to measure the effectiveness of different features adaptively. Because for some query images, local features can obtain better retrieval results. However, for some other queried images, global features such as CNN features are used to obtain better retrieval results. For the same query image, we need to compare and quantify the validity of different features. Based on the above two key points, our work is summarized as follows: (1) Adaptive weighted feature fusion method based on graph model. In this method, the graph model unifies the distance measure in different scale space into one Graph, and uses the unified measure method Jaccard coefficient to measure the similarity of each picture. At the same time, in order to evaluate the validity of different features, we use PageRank algorithm to analyze the graph constructed by different features, and evaluate the validity of different features according to the distribution of PageRank values. Finally, according to the validity of the feature to the specific retrieval image, the adaptive weighted fusion of the graph constructed by different features is completed. According to the final fusion graph, we solve the final image retrieval ranking. (2) Adaptive multi-feature fusion method based on neighborhood similarity distribution. This method is based on the distribution of the image in the nearest neighbor space under the given visual features. The distance distribution of k-nearest neighbor is different for a specific query image with different features. We evaluate the effectiveness of different features by exploring the spatial distribution of k-nearest neighbors. We propose the concept of validity coefficient-REC (Rank Effectiveness Coefficient). REC reflects the validity of a feature to a specific image. The similarity of the original feature is weighted by the validity coefficient, and the score of the similarity after the fusion is obtained. According to the similarity score of fusion, the final result of image retrieval and sorting can be given.
【學位授予單位】：中國科學技術大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
【參考文獻】
相關期刊論文 前1條
1 周文罡;李厚強;盧亦娟;田奇;;Encoding Spatial Context for Large-Scale Partial-Duplicate Web Image Retrieval[J];Journal of Computer Science & Technology;2014年05期
，本文編號：2311654
本文鏈接：http://lk138.cn/shoufeilunwen/xixikjs/2311654.html
最近更新
教材專著
