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
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
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
Visual language modeling for image classification
Proceedings of the international workshop on Workshop on multimedia information retrieval
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
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
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
Efficient large-scale image data set exploration: visual concept network and image summarization
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
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When large-scale online images come into view, it is very attractive to incorporate visual concept network for image summarization, organization and exploration. In this paper, we have developed an automatic algorithm for visual concept network generation by determining the diverse visual similarity contexts between the image concepts. To learn more reliable inter-concept visual similarity contexts, the images with diverse visual properties are crawled from multiple sources and multiple kernels are combined to characterize the diverse visual similarity contexts between the images and handle the issue of sparse image distribution more effectively in the high-dimensional multi-modal feature space. Kernel canonical correlation analysis (KCCA) is used to characterize the diverse inter-concept visual similarity contexts more accurately, so that our visual concept network can have better coherence with human perception. A similarity-preserving visual concept network visualization technique is developed to assist users on assessing the coherence between their perceptions and the inter-concept visual similarity contexts determined by our algorithm. Our experimental results on large-scale image collections have observed very good results.