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Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Semantic based image retrieval: a probabilistic approach
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Explicit query formulation with visual keywords
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Configuration based scene classification and image indexing
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Minimally Supervised Acquis''n. of 3D Recog''n. Models from Cluttered Images
Minimally Supervised Acquis''n. of 3D Recog''n. Models from Cluttered Images
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Home photo indexing using learned visual keywords
VIP '02 Selected papers from the 2002 Pan-Sydney workshop on Visualisation - Volume 22
Linking images and keywords for semantics-based image retrieval
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
A framework for moderate vocabulary semantic visual concept detection
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Learning consumer photo categories for semantic retrieval
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Image classification for content-based indexing
IEEE Transactions on Image Processing
Factor graph framework for semantic video indexing
IEEE Transactions on Circuits and Systems for Video Technology
Proceedings of the 11th international ACM SIGACCESS conference on Computers and accessibility
Food for talk: photo frames to support social connectedness for elderly people in a nursing home
European Conference on Cognitive Ergonomics: Designing beyond the Product --- Understanding Activity and User Experience in Ubiquitous Environments
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To bridge the semantic gap in content-based image retrieval, detecting meaningful visual entities (e.g. faces, sky, foliage, buildings etc) in image content and classifying images into semantic categories based on trained pattern classifiers have become active research trends. In this paper, we present dual cascading learning frameworks that extract and combine intra-image and inter-class semantics for image indexing and retrieval.In the supervised learning version, support vector detectors are trained on semantic support regions without image segmentation. The reconciled and aggregated detection-based indexes then serve as input for support vector learning of image classifiers to generate class-relative image indexes. During retrieval, similarities based on both indexes are combined to rank images.In the unsupervised learning approach, image classifiers are first trained on local image blocks from a small number of labeled images. Then local semantic patterns are discovered from clustering the image blocks with high classification output. Training samples are induced from cluster memberships for support vector learning to form local semantic pattern detectors. During retrieval, similarities based on local class pattern indexes and discovered pattern indexes are combined to rank images.Query-by-example experiments on 2400 unconstrained consumer photos with 16 semantic queries show that the combined matching aproaches are better than matching with single indexes. Both the supervised semantics design and the semantics discovery approaches also outperformed the linear fusion of color and texture features significantly in average precisions by 55% and 37% respectively.