Content-based image retrieval in botanical collections for gene expression studies

Content-based image retrieval has shown to be more and more useful for several application domains, from audiovisual media to security. As content-based retrieval became mature, different scientific applications were revealed client for such methods. More recently, botanical applications generated very large image collections and then became very demanding content-based visual similarity computation. In this paper, we describe low-level feature extraction for visual appearance comparison between genetically modified plants for gene expression studies.