Abstract: A method for automatically classifying images into a final set of events including receiving a first plurality of images having date-time and a second plurality of images with incomplete date-time information; determining one or more time differences of the first plurality of images based on date-time clustering of the images and classify the first plurality of images into a first set of possible events; analyzing the second plurality of images using scene content and metadata cues and selecting images which correspond to different events in the first set of possible events and combining them into their corresponding possible events to thereby produce a second set of possible events; and using image scene content to verify the second set of possible events and to change the classification of images which correspond to different possible events to thereby provide the final set of events.

Abstract: In methods and systems for classifying capture records, such as images. A collection of capture records is provided. Each capture record has metadata defining a map location. This metadata can be earlier determined from a stream of data transmissions, even if there are gaps in transmission. The provided capture records are clustered into groups based on capture locations. A map, inclusive of the capture locations, is segmented into a plurality of regions based on relative positions of the capture locations associated with each group. The regions are associated with the capture records of respective groups.

Abstract: In an image classification method, a plurality of grouping values are received. The grouping values each have an associated image. An average of the grouping values is calculated. A variance metric of the grouping values, relative to the average is computed. A grouping threshold is determined from the variance metric. Grouping values beyond the grouping threshold are identified as group boundaries. The images are assigned to a plurality of groups based upon the group boundaries.

Abstract: In a method for classifying a sequence of records into events based upon feature values, such as time and/or location, associated with each of the records, feature differences between consecutive records are determined. The feature differences are ranked. A sequence of three or more clusters of feature differences is computed. The clusters are arranged in decreasing order of relative likelihood of respective feature differences representing separations between events. The records can be inclusive of images.

Abstract: A method for automatically classifying images into a final set of events including receiving a first plurality of images having date-time and a second plurality of images with incomplete date-time information; determining one or more time differences of the first plurality of images based on date-time clustering of the images and classify the first plurality of images into a first set of possible events; analyzing the second plurality of images using scene content and metadata cues and selecting images which correspond to different events in the first set of possible events and combining them into their corresponding possible events to thereby produce a second set of possible events; and using image scene content to verify the second set of possible events and to change the classification of images which correspond to different possible events to thereby provide the final set of events.