Anatomical Annotations for Drosophila Gene Expression Patterns via Multi-Dimensional Visual Descriptors Integration: Multi-Dimensional Feature Learning

In Drosophila gene expression pattern research, the in situ hybridization (ISH) image has become the standard technique to visualize and study the spatial distribution of RNA. To facilitate the search and comparison of Drosophila gene expression patterns during Drosophila embryogenesis, it is highly desirable to annotate the tissue-level anatomical ontology terms for ISH images. In ISH image annotations, the image content representation is crucial to achieve satisfactory results. However, existing methods mainly focus on improving the classification algorithms and only using simple visual descriptor. If we integrate the effective local and holistic visual descriptors via proper learning method, we can achieve more accurate image annotation results than using individual visual descriptor. We propose a novel structured sparsity-inducing norms based feature learning model to integrate the multi-dimensional visual descriptors for Drosophila gene expression patterns annotations. The new mixed norms are designed to learn the importance of different features from both local and global point of views. We successfully integrate six widely used visual descriptors to annotate the Drosophila gene expression patterns from the lateral, dorsal, and ventral views. The empirical results show that the proposed new method can effectively integrate different visual descriptors, and consistently outperforms related methods using the concatenated visual descriptors.

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