document: Drosophila gene expression pattern annotation using sparse features and term-term interactions 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 .
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