METHOD FOR EVENT-BASED SEMANTIC CLASSIFICATION
A method of automatically classifying images in a consumer digital image collection, includes generating an event representation of the image collection; computing global time-based features for each event within the hierarchical event representation; computing content-based features for each image in an event within the hierarchical event representation; combining content-based features for each image in an event to generate event-level content-based features; and using time-based features and content-based features for each event to classify an event into one of a pre-determined set of semantic categories.
1 . A method comprising:
generating, using a processor, time-based event boundaries detected in a plurality of images;
computing inter-event durations;
grouping events into clusters based on the inter-event durations; and
validating, using a rule-based system, that each event belongs to an associated cluster based on event level content based features.
2 . The method of claim 1 , wherein grouping events into clusters includes using density-based clustering.
3 . The method of claim 1 , wherein the inter-event durations span multiple days.
4 . The method of claim 1 , wherein the inter-event durations span small duration gaps.
5 . The method of claim 4 , wherein the small duration gaps are less than 18 hours.
6 . The method of claim 1 , wherein validating, using a rule-based system, comprises referencing a database of auxiliary factual information associated with subjects identified in the plurality of images.
7 . The method of claim 1 , further comprising determining a location for each image in an event and grouping events into clusters based upon the locations.
8 . The method of claim 1 , further comprising determining a subject distance for each image in an event and grouping events into clusters based upon the determined locations.
9 . A system comprising:
one or more processors configured to:
generate time-based event boundaries detected in a plurality of images;
compute inter-event durations;
group events into clusters based on the inter-event durations; and
validate, using a rule-based system, that each event belongs to an associated cluster based on event level content based features.
10 . The system of claim 9 , wherein events are grouped into clusters using density-based clustering.
11 . The system of claim 9 , wherein the inter-event durations span multiple days.
12 . The system of claim 9 , wherein the inter-event durations span less than 18 hours.
13 . The system of claim 9 , wherein the validation includes referencing a database of auxiliary factual information associated with subjects identified in the plurality of images.
14 . The system of claim 9 , wherein the one or more processors are further configured to determine a location for each image in an event and group events into clusters based upon the locations.
15 . A non-transitory computer-readable medium having instructions stored thereon, the instructions comprising:
instructions to generate time-based event boundaries detected in a plurality of images;
instructions to compute inter-event durations;
instructions to group events into clusters based on the inter-event durations; and
instructions to validate, using a rule-based system, that each event belongs to an associated super-event cluster based on event level content based features.
16 . The non-transitory computer-readable medium of claim 15 , wherein events are grouped into clusters using density-based clustering.
17 . The non-transitory computer-readable medium of claim 15 , wherein the inter-event durations span multiple days.
18 . The non-transitory computer-readable medium of claim 15 , wherein the inter-event durations span less than 18 hours.
19 . The non-transitory computer-readable medium of claim 15 , wherein the instructions to validate includes instructions to reference a database of auxiliary factual information associated with subjects identified in the plurality of images.
20 . The non-transitory computer-readable medium of claim 15 , further comprising instructions to determine a location for each image in an event and group events into clusters based upon the locations.