Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
ACM Transactions on Knowledge Discovery from Data (TKDD)
Randomized Clustering Forests for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convex multi-task feature learning
Machine Learning
Drosophila gene expression pattern annotation through multi-instance multi-label learning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A shared-subspace learning framework for multi-label classification
ACM Transactions on Knowledge Discovery from Data (TKDD)
Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Trace Norm Regularization: Reformulations, Algorithms, and Multi-Task Learning
SIAM Journal on Optimization
Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks
ACM Transactions on Knowledge Discovery from Data (TKDD)
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The Drosophila gene expression pattern images document the spatial and temporal dynamics of gene expression and they are valuable tools for explicating the gene functions, interaction, and networks during Drosophila embryogenesis. To provide text-based pattern searching, the images in the Berkeley Drosophila Genome Project (BDGP) study are annotated with ontology terms manually by human curators. We present a systematic approach for automating this task, because the number of images needing text descriptions is now rapidly increasing. We consider both improved feature representation and novel learning formulation to boost the annotation performance. For feature representation, we adapt the bag-of-words scheme commonly used in visual recognition problems so that the image group information in the BDGP study is retained. Moreover, images from multiple views can be integrated naturally in this representation. To reduce the quantization error caused by the bag-of-words representation, we propose an improved feature representation scheme based on the sparse learning technique. In the design of learning formulation, we propose a local regularization framework that can incorporate the correlations among terms explicitly. We further show that the resulting optimization problem admits an analytical solution. Experimental results show that the representation based on sparse learning outperforms the bag-of-words representation significantly. Results also show that incorporation of the term-term correlations improves the annotation performance consistently.