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首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Harvesting Image Databases from the Web
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Harvesting Image Databases from the Web

机译：从Web收集图像数据库

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摘要

The objective of this work is to automatically generate a large number of images for a specified object class. A multimodal approach employing both text, metadata, and visual features is used to gather many high-quality images from the Web. Candidate images are obtained by a text-based Web search querying on the object identifier (e.g., the word penguin). The Webpages and the images they contain are downloaded. The task is then to remove irrelevant images and rerank the remainder. First, the images are reranked based on the text surrounding the image and metadata features. A number of methods are compared for this reranking. Second, the top-ranked images are used as (noisy) training data and an SVM visual classifier is learned to improve the ranking further. We investigate the sensitivity of the cross-validation procedure to this noisy training data. The principal novelty of the overall method is in combining text/metadata and visual features in order to achieve a completely automatic ranking of the images. Examples are given for a selection of animals, vehicles, and other classes, totaling 18 classes. The results are assessed by precision/recall curves on ground-truth annotated data and by comparison to previous approaches, including those of Berg and Forsyth [CHECK END OF SENTENCE] and Fergus et al. [CHECK END OF SENTENCE].
机译：这项工作的目的是为指定的对象类别自动生成大量图像。同时使用文本，元数据和视觉功能的多模式方法用于从Web收集许多高质量图像。候选图像是通过基于文本的Web搜索查询对象标识符（例如企鹅）来获得的。网页及其包含的图像已下载。然后的任务是删除不相关的图像并重新排列其余图像。首先，基于图像周围的文本和元数据功能对图像进行排名。比较了许多方法来进行这种排名。其次，将排名最高的图像用作（嘈杂的）训练数据，并学习SVM视觉分类器以进一步提高排名。我们调查了交叉验证程序对此嘈杂训练数据的敏感性。整体方法的主要新颖之处在于结合了文本/元数据和视觉特征，以实现图像的全自动排名。给出了选择动物，车辆和其他类别的示例，总共18个类别。通过对地面带注释数据的精确度/召回率曲线以及与包括Berg和Forsyth [CHECK END OF SENTENCE]和Fergus等人的先前方法进行比较来评估结果。 [检查句子结尾]。

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