Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Selection of Scale-Invariant Parts for Object Class Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling Scenes with Local Descriptors and Latent Aspects
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
AnnoSearch: Image Auto-Annotation by Search
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Integrating Co-Occurrence and Spatial Contexts on PatchBased Scene Segmentation
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Practical elimination of near-duplicates from web video search
Proceedings of the 15th international conference on Multimedia
VisualRank: Applying PageRank to Large-Scale Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIFT-Bag kernel for video event analysis
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Video event detection using motion relativity and visual relatedness
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Automatic selection of representative photo and smart thumbnailing using near-duplicate detection
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Feature classification for representative photo selection
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Automatic summarization of travel photos using near-duplication detection and feature filtering
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Real-time near-duplicate elimination for web video search with content and context
IEEE Transactions on Multimedia - Special issue on integration of context and content
Scale-rotation invariant pattern entropy for keypoint-based near-duplicate detection
IEEE Transactions on Image Processing
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Near-Duplicate Keyframe Identification With Interest Point Matching and Pattern Learning
IEEE Transactions on Multimedia
Multimodal News Story Clustering With Pairwise Visual Near-Duplicate Constraint
IEEE Transactions on Multimedia
Travelmedia: An intelligent management system for media captured in travel
Journal of Visual Communication and Image Representation
A configurable photo browser framework for large image collections
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: design and development approaches - Volume Part I
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Near-duplicate detection techniques are exploited to facilitate representative photo selection and region-of-interest (ROI) determination, which are important functionalities for efficient photo management and browsing. To make near-duplicate detection module resist to noisy features, three filtering approaches, i.e., point-based, region-based, and probabilistic latent semantic (pLSA), are developed to categorize feature points. For the photos taken in travels, we construct a support vector machine classifier to model matching patterns between photos and determine whether photos are near-duplicate pairs. Relationships between photos are then described as a graph, and the most central photo that best represents a photo cluster is selected according to centrality values. Because matched feature points are often located in the interior or at the contour of important objects, the region that compactly covers the matched feature points is determined as the ROI. We compare the proposed approaches with conventional ones and demonstrate their effectiveness.