Learning object categories from Google's image search
Learning object categories from Google's image search
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DOI：
10.1109/iccv.2005.142
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发表时间：
2005-10
期刊：
影响因子：
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通讯作者：
R. Fergus;Li Fei-Fei-Li-Fei-Fei-48004138;P. Perona;Andrew Zisserman
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文献类型：
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作者：
R. Fergus;Li Fei-Fei-Li-Fei-Fei-48004138;P. Perona;Andrew Zisserman
目前的对象类别识别方法需要手动准备训练图像的数据集，并进行不同程度的监督。我们提出了一种方法，可以学习的对象类别，从它的名字，通过利用互联网上的图像搜索引擎的原始输出。我们开发了一个新的模型，TSI-pLSA，扩展pLSA（适用于视觉词），包括空间信息的平移和尺度不变的方式。我们的方法可以处理高的类内变异性和大比例的不相关的图像返回的搜索引擎。我们在标准测试集上评估轮胎模型，显示出与现有方法在手工准备的数据集上训练的性能竞争力
Current approaches to object category recognition require datasets of training images to be manually prepared, with varying degrees of supervision. We present an approach that can learn an object category from just its name, by utilizing the raw output of image search engines available on the Internet. We develop a new model, TSI-pLSA, which extends pLSA (as applied to visual words) to include spatial information in a translation and scale invariant manner. Our approach can handle the high intra-class variability and large proportion of unrelated images returned by search engines. We evaluate tire models on standard test sets, showing performance competitive with existing methods trained on hand prepared datasets