An automatic hierarchical image classification scheme
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
International Journal of Computer Vision
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Hierarchical classification for automatic image annotation
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Canonical image selection from the web
Proceedings of the 6th ACM international conference on Image and video retrieval
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
MM '08 Proceedings of the 16th ACM international conference on Multimedia
An interactive approach for filtering out junk images from keyword-based google search results
IEEE Transactions on Circuits and Systems for Video Technology
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Generating visual concept network from large-scale weakly-tagged images
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
A probabilistic framework for semantic video indexing, filtering,and retrieval
IEEE Transactions on Multimedia
Can High-Level Concepts Fill the Semantic Gap in Video Retrieval? A Case Study With Broadcast News
IEEE Transactions on Multimedia
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When large-scale online images come into view, it is very important to construct a framework for efficient data exploration. In this paper, we build exploration models based on two considerations: inter-concept visual correlation and intra-concept image summarization. For inter-concept visual correlation, we have developed an automatic algorithm to generate visual concept network which is characterized by the visual correlation between image concept pairs. To incorporate reliable inter-concept correlation contexts, multiple kernels are combined and a kernel canonical correlation analysis algorithm is used to characterize the diverse visual similarity contexts between the image concepts. For intra-concept image summarization, we propose a greedy algorithm to sequentially pick the best representation of the image concept set. The quality score for each candidate summary is computed based on the clustering result, which considers the relevancy, orthogonality and uniformity terms at the same time. Visualization techniques are developed to assist user on assessing the coherence between concept-pairs and investigating the visual properties within the concept. We have conducted experiments and user studies to evaluate both algorithms. We observed very good results and received positive feedback.