Abstract: Analyzing digital content associated with a entity to identifier the entity's interest and influence related thereto. A set of digital content, such as text, pictures, or video, which the entity has submitted to one or more social media services, is identified. A set of interests for the entity is identified. This may be performed, in part, by analyzing a set of pictures to identify a set of visual attributes for each picture. Then, one or more descriptive labels that characterize the content shown in each picture are determined based on the set of visual attributes for each picture. Next, one or more subject categories for each picture are determined based upon the one or more descriptive labels for each picture. Thereafter, a set of actual interests for the entity is identified based, at least in part, upon the one or more subject categories associated with each digital picture.

Abstract: Massive amounts of multimedia data are stored in databases supporting web pages and servers, including text, graphics, video and audio. Searching and finding matching multimedia images can be time and computationally intensive. A method for storing and retrieving image data includes computing a descriptor, such an a Fourier-Mellin Transform (FMT), corresponding to a multidimensional space indicative of each of the stored images and organizing each of the descriptors according to a set similarity metric. The set similarity metric is based on Locality-Sensitive Hashing (LSH), and orders descriptors near to other descriptors in the database. The set similarity metric employs set theory which allows distance between descriptors to be computed consistent with LSH. A target image for which a match is sought is then received, and a descriptor indicative of the target image is computed. The database is referenced, or mapped, to determine close matches in the database.

Abstract: Massive amounts of multimedia data are stored in databases supporting web pages and servers, including text, graphics, video and audio. Searching and finding matching multimedia images can be time and computationally intensive. A method for storing and retrieving image data includes computing a descriptor, such an a Fourier-Mellin Transform (FMT), corresponding to a multidimensional space indicative of each of the stored images and organizing each of the descriptors according to a set similarity metric. The set similarity metric is based on Locality-Sensitive Hashing (LSH), and orders descriptors near to other descriptors in the database. The set similarity metric employs set theory which allows distance between descriptors to be computed consistent with LSH. A target image for which a match is sought is then received, and a descriptor indicative of the target image is computed. The database is referenced, or mapped, to determine close matches in the database.