Automatic image annotation using adaptive color classification
Graphical Models and Image Processing
Features and classification methods to locate deciduous trees in images
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Detection with Embedded Confidence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decoding Image Semantics Using Composite Region Templates
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
A Hierarchical Field Framework for Unified Context-Based Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Exploiting spatial context constraints for automatic image region annotation
Proceedings of the 15th international conference on Multimedia
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Probabilistic spatial context models for scene content understanding
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A probabilistic framework for semantic video indexing, filtering,and retrieval
IEEE Transactions on Multimedia
Scene Parsing Using Region-Based Generative Models
IEEE Transactions on Multimedia
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Factor graph framework for semantic video indexing
IEEE Transactions on Circuits and Systems for Video Technology
Event recognition: viewing the world with a third eye
MM '08 Proceedings of the 16th ACM international conference on Multimedia
MM '09 Proceedings of the 17th ACM international conference on Multimedia
A visual analysis of the relationship between word concepts and geographical locations
Proceedings of the ACM International Conference on Image and Video Retrieval
Context-aware classification for incremental scene interpretation
Proceedings of the Workshop on Use of Context in Vision Processing
Scene categorization via contextual visual words
Pattern Recognition
Rich location-driven tag cloud suggestions based on public, community, and personal sources
Proceedings of the 1st ACM international workshop on Connected multimedia
Collection-based sparse label propagation and its application on social group suggestion from photos
ACM Transactions on Intelligent Systems and Technology (TIST)
Geotagging in multimedia and computer vision--a survey
Multimedia Tools and Applications
Inferring photographic location using geotagged web images
Multimedia Tools and Applications
Fusing concept detection and geo context for visual search
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Find you wherever you are: geographic location and environment context-based pedestrian detection
Proceedings of the ACM multimedia 2012 workshop on Geotagging and its applications in multimedia
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Recent research has shown the power of context-aware scene understanding in bridging the semantic gap between high-level semantic concepts and low-level image features. In this paper, we present a new method to exploit nonvisual context information from the season and location proximity in which pictures were taken to facilitate region (object) annotation in consumer photos. Our method does not require precise time and location from the capture device or user input. Instead, it learns from rough location (e.g., states in the US) and time (e.g., seasons) information, which can be obtained through picture metadata automatically or through minimal user input (e.g., grouping). In addition, the visual context within the image is obtained by analyzing the spatial relationships between different regions (objects) in the scene. Both visual and nonvisual context information are fused using a probabilistic graphical model to improve the accuracy of object region recognition. Our method has been evaluated on a database that consists of over 10,000 regions in more than 1000 images collected from both the Web and consumers. Experimental results show that incorporating the season and location context significantly improves the performance of region recognition.