社交网络图像中LOGO检测与识别
发布时间：2018-10-21 11:58
【摘要】：近些年来随着社交网络的快速发展以及普及，人们将越来越多的时间放到了社交网络上，这使得社交网络成为最有潜力的广告以及商业平台。品牌跟踪是近些年出现的一种服务，通过分析品牌在媒体上曝光的频繁程度以及用户的评价来评估品牌的成长。由于社交网络的发展，品牌跟踪逐渐将重心转移到社交网络上。 对于品牌跟踪，在当前社交网络平台上仅仅提供通用的关键词搜索功能，这带来两个弊端：第一搜索结果中含有大量噪声，尽管含有关键词，，但经常与该品牌并不相关；第二大量含有品牌图片的信息无法被检索到。为了解决问题二，本文提出一种新的LOGO(商标)检测方法，将社交网络中用户上传的包含品牌LOGO的图像检测出来。这既可以作为一个独立的应用，直接作为品牌跟踪功能；也可以作为一个品牌分析系统的一部分。 社交网络上的图像有较大的比例为用户自己拍摄上传，图像质量往往较低，包括光线条件差、图像模糊、拍摄角度差，这使得图像中的LOGO发生光照不均匀、倾斜旋转、弹性变形、部分被遮挡等问题。此外，为了增加辨识度，LOGO往往被设计成简单的图形，这使得其与自然图像中的物体外形相似。这些都增加了LOGO检测的难度。为了解决社交网络图像的LOGO检测问题，本文研究一种基于机器学习的LOGO检测方法并评估其在社交网络上的应用。 本文主要贡献如下，一方面，本文建立了一个包含100个品牌LOGO的图像训练集以及测试集。其中训练集给出LOGO的位置、大小以及其旋转角度。测试图像包括100万张图像，每张图像已经标注好是否含有LOGO，以及LOGO的位置和大小。训练集中每个LOGO的样本数量平均超过300张。该数据集涵盖了LOGO在不同光照、面内旋转、模糊、拍摄角度的情况，对后续科研人员进行使用并测试具有很大的价值。另一方面，本文使用了一种新的LOGO检测算法。由于本课题采用机器学习的方法进行LOGO检测，这是一个正负样本严重不均衡的问题。而训练的过程中指定正负样本比例，因此本课题提出将每一级AdaBoost的节点选择出来的特征作为输入，得到一个线性分类器，克服正负样本不均衡的情况。最后本文给出一种基于LOGO检测算法新的品牌跟踪的方法，通过判断社交网络图像中是否含有LOGO来给出品牌的关注程度，给出阶段性的品牌关注度分析，从而补充了现有基于文本关键词的缺陷。
[Abstract]:With the rapid development and popularity of social networks in recent years, people will spend more and more time on social networks, which makes social networks the most potential advertising and business platform. Brand tracking is a service emerging in recent years. It evaluates brand growth by analyzing the frequency of brand exposure in the media and the evaluation of users. Due to the development of social network, brand tracking is gradually shifting its focus to social network. For brand tracking, only general keyword search function is provided on the current social network platform, which brings two disadvantages: the first search result contains a lot of noise, although it contains keywords, it is often not related to the brand; The second large amount of information containing brand images cannot be retrieved. In order to solve the second problem, this paper proposes a new LOGO (trademark) detection method, which detects the images uploaded by users in social networks including brand LOGO. This can be used as an independent application, directly as a brand tracking function, or as part of a brand analysis system. There is a large proportion of images taken and uploaded by users on social networks, and the quality of the images is often low, including poor light conditions, blurred images, poor shooting angles, which results in uneven illumination and skewed rotation of the LOGO in the images. Elastic deformation, partial occlusion and so on. In addition, in order to increase the degree of identification, LOGO is often designed as a simple figure, which makes it similar to the shape of objects in natural images. All of these increase the difficulty of LOGO detection. In order to solve the problem of LOGO detection of social network images, this paper studies a LOGO detection method based on machine learning and evaluates its application in social network. The main contributions of this paper are as follows: on the one hand, we build an image training set including 100 brand LOGO and test set. The training set gives the position, size and rotation angle of LOGO. The test image consists of 1 million images, each of which has been marked with the location and size of LOGO, and LOGO. The average number of samples per LOGO in the training set is more than 300. This data set covers the situation of LOGO in different illumination, in-plane rotation, blur and shooting angle, so it is of great value for the subsequent researchers to use and test. On the other hand, this paper uses a new LOGO detection algorithm. In this paper, machine learning is used to detect LOGO, which is a serious imbalance of positive and negative samples. The proportion of positive and negative samples is specified in the process of training, so this paper presents a linear classifier to overcome the imbalance of positive and negative samples by using the selected features of each level of AdaBoost as input. Finally, this paper presents a new brand tracking method based on LOGO detection algorithm. By judging whether there is LOGO in the image of social network, this paper gives the attention degree of the brand, and gives the stage analysis of brand concern. Thus, it complements the existing defects based on text keywords.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP391.41
[Abstract]:With the rapid development and popularity of social networks in recent years, people will spend more and more time on social networks, which makes social networks the most potential advertising and business platform. Brand tracking is a service emerging in recent years. It evaluates brand growth by analyzing the frequency of brand exposure in the media and the evaluation of users. Due to the development of social network, brand tracking is gradually shifting its focus to social network. For brand tracking, only general keyword search function is provided on the current social network platform, which brings two disadvantages: the first search result contains a lot of noise, although it contains keywords, it is often not related to the brand; The second large amount of information containing brand images cannot be retrieved. In order to solve the second problem, this paper proposes a new LOGO (trademark) detection method, which detects the images uploaded by users in social networks including brand LOGO. This can be used as an independent application, directly as a brand tracking function, or as part of a brand analysis system. There is a large proportion of images taken and uploaded by users on social networks, and the quality of the images is often low, including poor light conditions, blurred images, poor shooting angles, which results in uneven illumination and skewed rotation of the LOGO in the images. Elastic deformation, partial occlusion and so on. In addition, in order to increase the degree of identification, LOGO is often designed as a simple figure, which makes it similar to the shape of objects in natural images. All of these increase the difficulty of LOGO detection. In order to solve the problem of LOGO detection of social network images, this paper studies a LOGO detection method based on machine learning and evaluates its application in social network. The main contributions of this paper are as follows: on the one hand, we build an image training set including 100 brand LOGO and test set. The training set gives the position, size and rotation angle of LOGO. The test image consists of 1 million images, each of which has been marked with the location and size of LOGO, and LOGO. The average number of samples per LOGO in the training set is more than 300. This data set covers the situation of LOGO in different illumination, in-plane rotation, blur and shooting angle, so it is of great value for the subsequent researchers to use and test. On the other hand, this paper uses a new LOGO detection algorithm. In this paper, machine learning is used to detect LOGO, which is a serious imbalance of positive and negative samples. The proportion of positive and negative samples is specified in the process of training, so this paper presents a linear classifier to overcome the imbalance of positive and negative samples by using the selected features of each level of AdaBoost as input. Finally, this paper presents a new brand tracking method based on LOGO detection algorithm. By judging whether there is LOGO in the image of social network, this paper gives the attention degree of the brand, and gives the stage analysis of brand concern. Thus, it complements the existing defects based on text keywords.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP391.41
【共引文献】
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