Machine Learning
Image classification and querying using composite region templates
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
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Learning and inferring a semantic space from user's relevance feedback for image retrieval
Proceedings of the tenth ACM international conference on Multimedia
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
From Images to Sentences via Spatial Relations
SPELMG '99 Proceedings of the Integration of Speech and Image Understanding
Semantic Organization of Scenes Using Discriminant Structural Templates
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
The Journal of Machine Learning Research
Confidence-based dynamic ensemble for image annotation and semantics discovery
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Multi-level annotation of natural scenes using dominant image components and semantic concepts
Proceedings of the 12th annual ACM international conference on Multimedia
Fuzzy conceptual graphs for matching images of natural scenes
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Narrowing the semantic gap - improved text-based web document retrieval using visual features
IEEE Transactions on Multimedia
Image classification for content-based indexing
IEEE Transactions on Image Processing
Tracking video objects in cluttered background
IEEE Transactions on Circuits and Systems for Video Technology
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Delivering online advertisements inside images
MM '08 Proceedings of the 16th ACM international conference on Multimedia
AMR'10 Proceedings of the 8th international conference on Adaptive Multimedia Retrieval: context, exploration, and fusion
A MapReduce-based distributed SVM algorithm for automatic image annotation
Computers & Mathematics with Applications
A MapReduce-based distributed SVM ensemble for scalable image classification and annotation
Computers & Mathematics with Applications
A novel framework for concept detection on large scale video database and feature pool
Artificial Intelligence Review
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High-dimensional visual features for image content characterization enables effective image classification. However, training accurate image classifiers in high-dimensional feature space suffers from the problem of curse of dimensionality and thus requires a large number of labeled images. To achieve accurate classifier training in high-dimensional feature space, we propose a hierarchical feature subset selection algorithm for semantic image classification, where the feature subset selection procedure is seamlessly integrated with the underlying classifier training procedure in a single algorithm. First, our hierarchical feature subset selection framework partitions the high-dimensional feature space into multiple homogeneous feature subspaces and forms a two-level feature hierarchy. Second, weak image classifiers are trained for each homogeneous feature subspace at the lower level of the feature hierarchy, where the traditional feature subset selection techniques such as principal component analysis (PCA) can be used for dimension reduction. Finally, these weak classifiers are boosted to determine an optimal image classifier and the higher-level feature subset selection is realized by selecting the most effective weak classifiers and their corresponding homogeneous feature subsets. Our experiments on a specific domain of natural images have obtained very positive results.