Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Leveraging context to resolve identity in photo albums
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
A context-aware solution to annotate people in mobile devices
Proceedings of the 7th International Conference on Advances in Mobile Computing and Multimedia
Seeing people in social context: recognizing people and social relationships
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Face image retrieval across age variation using relevance feedback
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Classification based group photo retrieval with bag of people features
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Detection of photos from the same event captured by distinct cameras
Proceedings of the 18th Brazilian symposium on Multimedia and the web
Improving face recognition with genealogical and contextual data
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
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This paper proposes an image retrieval method which retrieves images of a specific person from group photos. Many query-by-example methods have focused only on the visual features of the queried person. However, since socially related people such as family and friends are often taken photos together, their co-occurrence relations can be useful information. Thus, we propose an image retrieval method which uses the visual features of not only the queried person but also those who co-occur with the queried person in the same images. Relevance feedback is used to learn who co-occur with the queried person, their faces, and how strong their co-occurrence relations are. When retrieving the images of 19 persons in total from 158 images, after five feedback iterations, the recall rate of 50% was obtained by considering the people co-occurrence relations, as against 33% when considering only the queried person. With human errors in giving relevance feedback, the recall rate still improved to 40%.