Abstract: Embodiments of the present disclosure a method for determining product relevancy including extracting metadata from an image file of a digital image collection, the metadata being indicative of at least one feature of the image file. The method includes creating an input profile corresponding to the metadata extracted from the image files of the digital image collection. The method includes comparing the input profile to a product profile, the product profile having one or more rules corresponding to a consumer product, wherein the rules are indicative of the requirements of the product. The method includes determining a match score, the match score indicative of a relevancy of the product profile to the input profile such that a high relevancy correlates to a consumer product that is suited to the input profile and a low relevancy correlates to the consumer product that is not suited to the input profile.

Abstract: The invention relates generally to the field of digital image processing, and in particular to a method for associating and viewing related video and still images. In particular, the present invention is directed to methods for associating and/or viewing digital content records comprising ordering a first set of digital content records and the second set of digital content records based upon information associated with each of the digital content records.

Abstract: A system that is capable of generating a multiplicity of representations from a set of multimedia objects, each with a potentially different form, is disclosed. In addition, a system that is capable of generating representations that include thematic groupings in addition to the traditional purely sequential groupings is disclosed. Analysis of metadata to generate derived metadata creates a more comprehensive repository of information about media assets. Using the abundance of metadata, embodiments of the methods disclosed teach uniquely grouping and prioritizing media assets into a storyboard, which is in turn mapped onto view-based representations based on a selected output modality.

Abstract: A method for viewing a collection of images or videos, includes analyzing the collection to determine properties of the images or videos and using the determined properties to produce icons corresponding to such properties; providing a time-varying display of the images or videos in the collection following an ordering of the images or videos in the collection and at least one of the corresponding icons; receiving a user selection of an icon; and changing the display of the images or videos in the collection following a reordering of the images or videos in the collection in response to the user selection.

Abstract: A method of identifying groups of related digital images in a digital image collection, comprising: analyzing each of the digital images to generate associated feature descriptors related to image content or image capture conditions; storing the feature descriptors associated with the digital images in a metadata database; automatically analyzing the metadata database to identify a plurality of frequent itemsets, wherein each of the frequent itemsets is a co-occurring feature descriptor group that occurs in at least a predefined fraction of the digital images; determining a probability of occurrence for each the identified frequent itemsets; determining a quality score for each of the identified frequent itemsets responsive to the determined probability of occurrence; ranking the frequent itemsets based at least on the determined quality scores; and identifying one or more groups of related digital images corresponding to one or more of the top ranked frequent itemsets.

Abstract: A system for ranking events in media collections includes a processor-accessible memory for storing a media collection, and a processor for clustering the media collection items into a hierarchical event structure, for identifying and visually counting similar sub-events within each event in the hierarchical event structure, for determining a ranking of events based on the count of sub-events within each event, and for associating the determined ranking with each event in the media collection.

Abstract: A method for ranking events in media collections includes designating a media collection, using a processor to cluster the media collection items into a hierarchical event structure, using the processor to identify and count visually similar sub-events within each event in the hierarchical event structure, using the processor to determine a ranking of events based on the count of sub-events within each event, and associating the determined ranking with each event in the media collection.

Abstract: A method for viewing a collection of images or videos, includes analyzing the collection to determine properties of the images or videos and using the determined properties to produce icons corresponding to such properties; providing a time-varying display of the images or videos in the collection following an ordering of the images or videos in the collection and at least one of the corresponding icons; receiving a user selection of an icon; changing the time-varying display of the images or videos in the collection following a reordering of the images or videos in the collection in response to the user selection; storing the sequence of the user selections and associated timing in a script in a processor accessible memory; and playing back the viewing of the collection of images or videos using the script.

Abstract: A method of detecting recurring events in a digital image collection taken over a pre-determined period of time is disclosed. The method uses a processor for analyzing the digital image collection to produce a two-dimensional representation of the distribution of image capture activity over time and detecting recurring events by identifying spatial clusters in the two-dimensional representation.

Abstract: A method for organizing an event timeline for a digital image collection, includes using a processor for detecting events in the digital image collection and each event's associated timespan; determining the detected events that are significant in the digital image collection; and organizing the event timeline so that the event timeline shows the significant events and a clustered representation of the other events, made available to the user at different time granularities. The organized event timeline is also used for selecting images for generating output.

Abstract: A method for organizing an event timeline for a digital image collection, includes using a processor for detecting events in the digital image collection and each event's associated timespan; determining the detected events that are significant in the digital image collection; and organizing the event timeline so that the event timeline shows the significant events and a clustered representation of the other events, made available to the user at different time granularities. The organized event timeline is also used for selecting images for generating output.

Abstract: A method of identifying groups of related digital images in a digital image collection, comprising: analyzing each of the digital images to generate associated feature descriptors related to image content or image capture conditions; storing the feature descriptors associated with the digital images in a metadata database; automatically analyzing the metadata database to identify a plurality of frequent itemsets, wherein each of the frequent itemsets is a co-occurring feature descriptor group that occurs in at least a predefined fraction of the digital images; determining a probability of occurrence for each the identified frequent itemsets; determining a quality score for each of the identified frequent itemsets responsive to the determined probability of occurrence; ranking the frequent itemsets based at least on the determined quality scores; and identifying one or more groups of related digital images corresponding to one or more of the top ranked frequent itemsets.

Abstract: A method of detecting recurring events in a digital image collection taken over a pre-determined period of time is disclosed. The method uses a processor for analyzing the digital image collection to produce a two-dimensional representation of the distribution of image capture activity over time and detecting recurring events by identifying spatial clusters in the two-dimensional representation.

Abstract: 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.

Abstract: A method of detecting recurring events in a digital image collection taken over a pre-determined period of time is disclosed. The method uses a processor for analyzing the digital image collection to produce a two-dimensional representation of the distribution of image capture activity over time and detecting recurring events by identifying spatial clusters in the two-dimensional representation.

Abstract: A method of identifying groups of related digital images in a digital image collection, comprising: analyzing each of the digital images to generate associated feature descriptors related to image content or image capture conditions; storing the feature descriptors associated with the digital images in a metadata database; automatically analyzing the metadata database to identify a plurality of frequent itemsets, wherein each of the frequent itemsets is a co-occurring feature descriptor group that occurs in at least a predefined fraction of the digital images; determining a probability of occurrence for each the identified frequent itemsets; determining a quality score for each of the identified frequent itemsets responsive to the determined probability of occurrence; ranking the frequent itemsets based at least on the determined quality scores; and identifying one or more groups of related digital images corresponding to one or more of the top ranked frequent itemsets.

Abstract: 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.

Abstract: A method for identifying digital images having matching backgrounds from a collection of digital images, comprising using a processor to perform the steps of: determining a set of one or more feature values for each digital image in the collection of digital images, wherein the set of feature values includes an edge compactness feature value that is an indication of the number of objects in the digital image that are useful for scene matching; determining a subset of the collection of digital images that are good candidates for scene matching by applying a classifier responsive to the determined feature values; applying a scene matching algorithm to the subset of the collection of digital images to identify groups of digital images having matching backgrounds; and storing an indication of the identified groups of digital images having matching backgrounds in a processor-accessible memory.

Abstract: A method of identifying groups of related digital images in a digital image collection, comprising: analyzing each of the digital images to generate associated feature descriptors related to image content or image capture conditions; storing the feature descriptors associated with the digital images in a metadata database; automatically analyzing the metadata database to identify a plurality of frequent itemsets, wherein each of the frequent itemsets is a co-occurring feature descriptor group that occurs in at least a predefined fraction of the digital images; determining a probability of occurrence for each the identified frequent itemsets; determining a quality score for each of the identified frequent itemsets responsive to the determined probability of occurrence; ranking the frequent itemsets based at least on the determined quality scores; and identifying one or more groups of related digital images corresponding to one or more of the top ranked frequent itemsets.

Abstract: A method for determining significant events in a digital image collection, including, using a processor for generating image counts time-series from the image collection; computing a model of the image counts time-series; and using the image counts time-series and the model to determine significant events.