WIKImage:相关的图片和文字数据集WIKImage: Correlated image and text datasets |
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课程网址: | http://videolectures.net/sikdd2011_pracner_wikimage/ |
主讲教师: | Doni Pracner |
开课单位: | 诺维萨德大学 |
开课时间: | 2011-11-04 |
课程语种: | 日语 |
中文简介: | 本文提出了创建相关图像和文本的自由和可再分配数据集的工作。免费图片和相关文本的集合是用我们的新工具wikimage从wikipedia中提取的。引入了另一个工具–wikimage浏览器–,以可视化结果数据集,并将其扩展为手动标签工具。本文给出了一个1007个图像的起始数据集,其中有14个标记的任意组合。将图像处理成若干尺度不变(SIFT)和颜色直方图特征,并将标题转换成一袋文字(BOW)表示。然后进行实验,目的是用图像信息、文本数据以及两者对数据集变量上的每个标签的数据进行分类,以估计数据集在不同特征空间中的难度。结果表明,支持向量机组合表示和余弦相似度K-最近邻分类器在精度、召回率和F-度量上均有所提高。 |
课程简介: | This paper presents work towards the creation of free and redistributable datasets of correlated images and text. Collections of free images and related text were extracted from Wikipedia with our new tool WIKImage. An additional tool – WIKImage browser – was introduced to visualize the resulting dataset, and was expanded into a manual labeling tool. The paper presents a starting dataset of 1007 images labeled with any combination of 14 tags. The images were processed into a number of scale invariant (SIFT) and color histogram features, and the captions were transformed into a bag-of-words (BOW) representation. Experiments were then performed with the aim of classifying data with respect to each of the labels on dataset variants with just the image information, just the textual data, and both, in order to estimate the difficulty of the dataset in the context of different feature spaces. Results indicate improvements in precision, recall and the F-measure when using the combined representation with support vector machines as well as the k-nearest neighbor classifier with the cosine similarity measure. |
关 键 词: | 计算机科学; 文本挖掘; 机器学习; 图像分析 |
课程来源: | 视频讲座网 |
最后编审: | 2020-06-06:zyk |
阅读次数: | 78 |