Active shape models—their training and application
Computer Vision and Image Understanding
Statistical color models with application to skin detection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
FaceTracer: A Search Engine for Large Collections of Images with Faces
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Personalized photograph ranking and selection system
Proceedings of the international conference on Multimedia
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Seeing people in social context: recognizing people and social relationships
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Context-aided human recognition – clustering
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Automatic person annotation of family photo album
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Image ranking and retrieval based on multi-attribute queries
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Improving face recognition with genealogical and contextual data
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Hi-index | 0.00 |
Portraits, also known as images of people, constitute an important part of consumer photos. Existing methods manage portraits based on either explicit objectives, e.g., a specified person or event, or aesthetics, i.e., the aesthetic quality of portraits. This paper presents a novel system for personalized portraits ranking. First, four kinds of personalized features, i.e., composition, clothing style, affection and social relationship are proposed to quantify users' intent. Then, example-based and sketch-based user interfaces (UI) are developed, which are capable of capturing users' personal intent hardly described by queries or aesthetics. Finally, portraits ranking is implemented by combing these features together with the developed user interfaces. Experimental results show that the system performs well in providing personalized preferences and the proposed features are effective for portraits ranking. From the user study, our system gets promising results.