Classification based group photo retrieval with bag of people features

Abstract

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.