Classification based group photo retrieval with bag of people features
Classification based group photo retrieval with bag of people features
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DOI：
10.1145/2324796.2324804
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发表时间：
2012-06
期刊：
影响因子：
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通讯作者：
K. Shimizu;Naoko Nitta;Yujiro Nakai;N. Babaguchi
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文献类型：
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作者：
K. Shimizu;Naoko Nitta;Yujiro Nakai;N. Babaguchi
提出了一种从给定的群照图像集合中检索包含特定目标人物的图像的方法。这可以通过按示例查询的方法来实现，该方法将给定查询图像中的目标人的面部视觉特征与图像集合中的图像中的每个人的面部视觉特征进行比较。然而，由于图像通常是在不同的条件下拍摄的，同一个人的面部外观可能会有所不同。由于家庭和朋友等社会关系相关的人经常在一起拍照，所以同一张图像中的人的共现关系也可以作为图像检索的有用线索。针对这类人的共现关系，提出了一种既能表示人的面部特征，又能表示人在同一图像中的共现关系的人袋特征。通过使用BOP特征，可以从由用户的相关性反馈标记的少量图像中训练用于将图像分类为两类的分类器，即包含目标人物的图像和其他图像。此外，由于相关反馈得到的已标记图像比未标记图像在图像集合中的数量要少得多，因此使用主动学习方法来选择有用图像来训练分类器。当从550幅图像中检索24个人的图像时，经过5次反馈迭代，考虑人的共现关系的平均精度为0.94，而仅考虑目标人的平均精度为0.69。
This paper proposes a method for retrieving images containing a specific target person from a given image collection of group photos. This can be realized by query-by-example methods which compare the facial visual features of the target person in the given query image and of each person in the images in the image collection. However, since images are often taken under various conditions, facial appearance of the same person can vary. Since socially related people such as family and friends are often taken photos together, the people co-occurrence relations in the same images can also be a useful clue for image retrieval. Focusing on such people co-occurrence relations, we propose Bag of People (BoP) features which represent both the facial appearances of persons and their co-occurrence relations in the same images. By using the BoP features, a classifier for classifying images into two classes, images containing the target person and other images, can be trained from a small number of images labeled by user's relevance feedback. Furthermore, since the labeled images obtained by relevance feedback are much fewer than unlabeled images in the image collection, an active learning method is used to select useful images to train the classifier. When retrieving images of 24 persons in total from 550 images, after five feedback iterations, the mean average precision of 0.94 was obtained by considering the people co-occurrence relations, as against 0.69 when considering only the target person.