Image retrieval considering people co-occurrence relations using relevance feedback
Kazuya Shimizu, Naoko Nitta, Noboru Babaguchi · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
The recent popularity of digital cameras allows us to take a large number of images. There is an increasing need for efficiently and accurately retrieving images containing a specific person from such image collections. While only the visual features of the specific person are used in many query-by-example retrieval methods, we focus on the fact that some people such as family or friends are more likely to appear in the same images than others and use visual features of not only the queried person but also people who have strong co-occurrence relations with the queried person to improve the retrieval performance. The relevance feedback is used to learn who co-occur with the queried person in the same images, their faces, and the strength of their co-occurrence relations. For 116 images collected from 6 persons, after five feedback iterations, the recall rate of 53% was obtained by considering the co-occurrence relations among people, as against 34% when using only features of the queried person.