Abstract: A method for detecting an emotional state of a user includes obtaining a first data stream indicative of facial appearance and gaze direction of the user as the user is viewing a scene, determining, based on the first data stream, facial expression feature information indicative of emotional facial expression of the user, obtaining a second data stream indicative of visual content in a field of view of the user, determining, based on the second data stream, visual feature information indicative of visual content in the scene, determining emotional state information based on analyzing the facial expression feature information determined based on the first data stream and the visual feature information determined based on the second data stream, and performing an operation with respect to the emotional state information, wherein the emotional state information is indicative of the emotional state of the user.

Abstract: A method for detecting content of interest to a user includes obtaining a first data stream indicative of eye movement and/or gaze direction of the user as the user is viewing a scene in a field of view of the user, obtaining a second data stream indicative of visual content in the field of view of the user, determining, based on the first data stream and the second data stream, that content of interest to the user is present in the scene in the field of view of the user, and, in response to determining that content of interest to the user is present in the scene in the field of view of the user, triggering, with the processor, an operation to be performed with respect to the scene in the field of view of the user.

Abstract: A content-addressable and -searchable storage system for managing and exploring massive amounts of feature-rich data such as images, audio or scientific data, is shown. A segmentation and feature extraction unit segments data corresponding to an object into a plurality of data segments and -generates a feature vector for each data segment. A sketch construction component converts the feature vector into a compact bit-vector corresponding to the object. The system also has a similarity index having plurality of compact bit-vectors corresponding to a plurality of objects and an index insertion component for inserting a compact bit-vector corresponding to an object into the similarity index. The system may further have an indexing unit for identifying a candidate set of objects from said similarity index based upon a compact bit-vector corresponding to a query object.

Abstract: A computer-implemented method for searching a plurality of stored objects. Data objects are placed in a hash table, an ordered sequence of locations (probing sequence) in the hash table from a query object is generated and data objects in the hash table locations in the generated ordered sequence are examined to find objects whose relationships with the query object satisfy a certain predetermined function defined on pairs of objects.

Abstract: A content-addressable and searchable storage system for managing and exploring massive amounts of feature-rich data such as images, audio or scientific data, is shown. The system comprises a segmentation and feature extraction unit for segmenting data corresponding to an object into a plurality of data segments and generating a feature vector for each data segment; a sketch construction component for converting a feature vector into a compact bit-vector corresponding to the object; a similarity index comprising a plurality of compact bit-vectors corresponding to a plurality of objects; and an index insertion component for inserting a compact bit-vector corresponding to an object into the similarity index. The system may further comprise an indexing unit for identifying a candidate set of objects from said similarity index based upon a compact bit-vector corresponding to a query object.