An effective use of adaptive combination of visual features to retrieve image semantics from a hierarchical image database ?
Branch selection and pruning algorithms are proposed to assign semantics to images. Algorithms use <1/3 < 1 / 3 search space to retrieve general as well as specific semantics. Algorithms report ?0.7 precision on ImageNet using only ?10% of the search space. Using 1/4 space of WANG, attained precision is 0.94 compared to Li and Wang (0.64). The study explores the existing correlation among semantic categories in ImageNet.
visual features image semantics hierarchical image database semantic similarity visual similarity semantic based categorization visual search space search space pruning