Abstract: Various embodiments relate to methods, apparatuses and computer-readable mediums for organizing data relating to a product. An embodiment relates to a method for generating a modified hierarchy for a product based on data relating to the product. The method includes generating an initial hierarchy for the product, the initial hierarchy comprising a plurality of nodes, each node representing a different product aspect, the plurality of nodes being interconnected in dependence on relationships between different product aspects. The method also includes identifying a product aspect from the data. The method additionally includes determining an optimal position in the initial hierarchy for the identified product aspect by computing an objective function. The method further includes inserting the identified product aspect into the optimal position in the initial hierarchy to generate the modified hierarchy.

Abstract: Described is a technology by which an image is classified (e.g., grouped and/or labeled), based on multi-label multi-instance data learning-based classification according to semantic labels and regions. An image is processed in an integrated framework into multi-label multi-instance data, including region and image labels. The framework determines local association data based on each region of an image. Other multi-label multi-instance data is based on relationships between region labels of the image, relationships between image labels of the image, and relationships between the region and image labels. These data are combined to classify the image. Training is also described.

Abstract: Described is a technology by which an image is classified (e.g., grouped and/or labeled), based on multi-label multi-instance data learning-based classification according to semantic labels and regions. An image is processed in an integrated framework into multi-label multi-instance data, including region and image labels. The framework determines local association data based on each region of an image. Other multi-label multi-instance data is based on relationships between region labels of the image, relationships between image labels of the image, and relationships between the region and image labels. These data are combined to classify the image. Training is also described.