基于社交圈的移動(dòng)設(shè)備個(gè)性化圖像標(biāo)注
發(fā)布時(shí)間：2019-05-06 08:48
【摘要】：近年來,隨著用戶移動(dòng)設(shè)備中照片數(shù)量的爆炸性增長,用戶組織、管理、檢索這些個(gè)人照片變得十分困難。自動(dòng)為這些圖像提供標(biāo)簽,是解決該問題的一個(gè)有效途徑。不同于傳統(tǒng)基于內(nèi)容的圖像標(biāo)注算法,對于用戶移動(dòng)設(shè)備上的圖像標(biāo)注,用戶更關(guān)注于圖像的上下文信息。另一方面,隨著社交網(wǎng)絡(luò)和wifi/4G網(wǎng)絡(luò)的發(fā)展,大量的用戶圖像被上傳到社交平臺(tái)上。這些圖像基本都是拍攝于用戶的日常生活中,因此用戶的社交圈信息可以為理解圖像的上下文信息提供有用的線索。在本文中,我們提出了一種基于用戶社交圈的移動(dòng)設(shè)備上的圖像個(gè)性化標(biāo)注框架。但是,由于社交網(wǎng)絡(luò)上的信息具有稀疏性和不準(zhǔn)確性,使得直接利用社交網(wǎng)絡(luò)進(jìn)行圖像標(biāo)注的效果并不好。為了解決這個(gè)問題,我們基于對社交網(wǎng)絡(luò)特性的研究,在為用戶個(gè)人照片提供標(biāo)簽之前,先為社交網(wǎng)絡(luò)上的圖像標(biāo)簽進(jìn)行修正,產(chǎn)生可靠的標(biāo)簽。考慮到事件是人們生活和記憶中最重要的一種組織方式,用戶上傳到社交網(wǎng)絡(luò)中的大多數(shù)照片都是拍攝于特定的事件,而相同的事件具有同樣的事件屬性標(biāo)簽,因此我們提出在社交網(wǎng)絡(luò)中發(fā)現(xiàn)事件,為圖像產(chǎn)生事件標(biāo)簽。我們采用基于多模態(tài)的層次聚類算法來發(fā)現(xiàn)社交網(wǎng)絡(luò)中的事件。同時(shí),我們創(chuàng)新性地引入了“相冊”的概念,并將其作為聚類的基本單元,而不是通常的以單張圖像作為聚類的基本單元。最后,我們基于相似的圖像更有可能有相同的標(biāo)簽這一思想,采用加權(quán)的K近鄰模型將修正后社交網(wǎng)絡(luò)中圖像的標(biāo)簽傳播給用戶移動(dòng)設(shè)備上的待標(biāo)注圖像,從而為用戶的個(gè)人照片自動(dòng)產(chǎn)生個(gè)性化的標(biāo)注。為了驗(yàn)證我們對于社交網(wǎng)絡(luò)特性的觀察和分析,我們在來源于Flickr的公開數(shù)據(jù)集ReSEED和我們自己在人人網(wǎng)上抓取的真實(shí)數(shù)據(jù)集兩個(gè)數(shù)據(jù)集上對社交網(wǎng)絡(luò)各方面的特性進(jìn)行研究分析,結(jié)果證實(shí)了我們的觀察和假設(shè)。同時(shí),我們也在數(shù)據(jù)集上對我們提出的算法進(jìn)行了測試,并與其它算法進(jìn)行比較,實(shí)驗(yàn)結(jié)果顯示我們的算法有著較好的性能。
[Abstract]:In recent years, with the explosive increase in the number of photos in user mobile devices, it is very difficult for users to organize, manage and retrieve these personal photos. Automatically providing labels for these images is an effective way to solve this problem. Different from the traditional content-based image tagging algorithm, users pay more attention to the context information of the image than the traditional content-based image tagging algorithm. On the other hand, with the development of social networks and wifi/4G networks, a large number of user images are uploaded to social platforms. These images are basically photographed in the daily life of the user, so the social circle information of the user can provide a useful clue to understand the context information of the image. In this paper, we propose a personalized image annotation framework for mobile devices based on user social circle. However, because of the sparsity and inaccuracy of the information on the social network, it is not good to use the social network to annotate the image directly. In order to solve this problem, based on the study of the characteristics of social networks, we first modify the image tags on social networks to produce reliable tags before providing tags for users' personal photos. Considering that events are one of the most important forms of organization in people's lives and memories, most of the photos that users upload to social networks are taken on specific events, and the same events have the same event attribute labels, Therefore, we propose to discover events in social networks and generate event tags for images. We use multi-modal hierarchical clustering algorithm to discover events in social networks. At the same time, we introduce the concept of "photo album" creatively, and regard it as the basic unit of clustering, instead of taking a single image as the basic unit of clustering. Finally, based on the idea that similar images are more likely to have the same tags, we adopt a weighted K-nearest neighbor model to propagate the tags of the images in the modified social network to the images to be labeled on the user's mobile devices. Thus for the user's personal photos automatically generate personalized tagging. To validate our observations and analysis of social networking features, we studied and analyzed various aspects of social networking features on two datasets, ReSEED, an open data set from Flickr, and a real dataset we ourselves captured on Renren. The results confirm our observations and assumptions. At the same time, we also test the proposed algorithm on the data set and compare it with other algorithms. The experimental results show that our algorithm has a good performance.
【學(xué)位授予單位】：清華大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.41
本文編號：2470041
[Abstract]:In recent years, with the explosive increase in the number of photos in user mobile devices, it is very difficult for users to organize, manage and retrieve these personal photos. Automatically providing labels for these images is an effective way to solve this problem. Different from the traditional content-based image tagging algorithm, users pay more attention to the context information of the image than the traditional content-based image tagging algorithm. On the other hand, with the development of social networks and wifi/4G networks, a large number of user images are uploaded to social platforms. These images are basically photographed in the daily life of the user, so the social circle information of the user can provide a useful clue to understand the context information of the image. In this paper, we propose a personalized image annotation framework for mobile devices based on user social circle. However, because of the sparsity and inaccuracy of the information on the social network, it is not good to use the social network to annotate the image directly. In order to solve this problem, based on the study of the characteristics of social networks, we first modify the image tags on social networks to produce reliable tags before providing tags for users' personal photos. Considering that events are one of the most important forms of organization in people's lives and memories, most of the photos that users upload to social networks are taken on specific events, and the same events have the same event attribute labels, Therefore, we propose to discover events in social networks and generate event tags for images. We use multi-modal hierarchical clustering algorithm to discover events in social networks. At the same time, we introduce the concept of "photo album" creatively, and regard it as the basic unit of clustering, instead of taking a single image as the basic unit of clustering. Finally, based on the idea that similar images are more likely to have the same tags, we adopt a weighted K-nearest neighbor model to propagate the tags of the images in the modified social network to the images to be labeled on the user's mobile devices. Thus for the user's personal photos automatically generate personalized tagging. To validate our observations and analysis of social networking features, we studied and analyzed various aspects of social networking features on two datasets, ReSEED, an open data set from Flickr, and a real dataset we ourselves captured on Renren. The results confirm our observations and assumptions. At the same time, we also test the proposed algorithm on the data set and compare it with other algorithms. The experimental results show that our algorithm has a good performance.
【學(xué)位授予單位】：清華大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.41
【參考文獻(xiàn)】
相關(guān)期刊論文 前1條
1 路晶;馬少平;;使用基于多例學(xué)習(xí)的啟發(fā)式SVM算法的圖像自動(dòng)標(biāo)注[J];計(jì)算機(jī)研究與發(fā)展;2009年05期
，本文編號：2470041
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