Abstract: An image-record subject is identified for a plurality of image records in an image collection. Then, a sampling change-metric related to a changing characteristic of the image-record subject is identified, and sampling of at least a portion of the image collection occurs at least according to the sampling change-metric to obtain one or more image records for the subset. Information pertaining to results of the sampling step is stored in a computer-accessible memory system.

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 display system and method for operating a display and a collection of digital multimedia objects are provided. A first selection set of predefined organizational metaphors is presented and a selection of a first organizational metaphor from the first selection set is received. A second selection set of predefined organizational metaphors other than the first selected organizational metaphor is presented and a selection of a second organizational metaphor from the second selection set is received. A result is presented on the display having one of at least two group icons, each group icon indicating a group of digital multimedia objects chosen from the collection according to rules associated with the selected organizational metaphors and the content of the digital multimedia objects or any metadata associated with the digital multimedia objects. Wherein the group of digital multimedia objects indicated by each group icon are chosen according to result presentation rules.

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 associating event times or time periods with digital images in a collection for determining if a digital image is of interest, includes storing a collection of digital images each having an associated capture time; comparing the associated capture time in the collection with a special event time to determine if a digital image in the collection is of interest, wherein the comparing step includes calculation of a special event time associated with a special event based on the calendar time associated with the special event and using such information to perform the comparison step; and associating digital images of interest with the special event.

Abstract: A method for determining a classification for a video segment, comprising the steps of: breaking the video segment into a plurality of short-term video slices, each including a plurality of video frames and an audio signal; analyzing the video frames for each short-term video slice to form a plurality of region tracks; analyzing each region track to form a visual feature vector and a motion feature vector; analyzing the audio signal for each short-term video slice to determine an audio feature vector; forming a plurality of short-term audio-visual atoms for each short-term video slice by combining the visual feature vector and the motion feature vector for a particular region track with the corresponding audio feature vector; and using a classifier to determine a classification for the video segment responsive to the short-term audio-visual atoms.

Abstract: A method for estimating the aesthetic quality of an input digital image comprising using a digital image processor for performing the following: determining one or more vanishing point(s) associated with the input digital image by automatically analyzing the digital image; computing a compositional model from at least the positions of the vanishing point(s); and producing an aesthetic quality parameter for the input digital image responsive to the compositional model, wherein the aesthetic quality parameter is an estimate for the aesthetic quality of the input digital image.

Abstract: A method for measuring a photographer's progress over time toward producing images with a high level of aesthetic quality by assessing the aesthetic quality of a set of digital images captured by the photographer comprising: providing a set of digital images captured by a particular photographer, each digital image having and associated capture times captured by a particular photographer; using a processor to compute an aesthetic quality parameters for each digital image in the set; and producing an indication of the photographer's progress toward producing images with a high level of aesthetic quality using the aesthetic quality parameters for each digital image in the set and the corresponding associated capture times.

Abstract: A method for comparing a plurality of photographers by assessing the aesthetic quality of a set of digital images captured by each photographer comprising: providing a set of digital images captured by each of a plurality of photographers; using a processor to determine an aesthetic quality parameter for each digital image in each of the sets of digital images, wherein the aesthetic quality parameter is an estimate for the aesthetic quality of the digital image; determining an aesthetic quality distribution for each photographer responsive to the aesthetic quality parameters computed for each of the digital images in the photographer's set of digital images; and providing a comparison between the aesthetic quality distributions of the photographers.

Abstract: In a computer-mediated method for providing representative images, image records are classified spatio-temporally into groups. In each group, image records are partitioned into clusters and the hierarchically highest cluster is ascertained. The partitioning is between a hierarchy of feature clusters and a remainder cluster, based on a predetermined plurality of saliency features. Feature clusters each have one or more of the saliency features. The remainder cluster lacks the saliency features. Feature clusters are each exclusive of the saliency features of any higher clusters in the hierarchy and non-exclusive of the saliency features of any lower feature clusters in the hierarchy. A representative image of each group is designated from respective image records based on: the respective saliency feature of the highest cluster when the highest cluster is a feature cluster and independent of the saliency features when the highest cluster is the remainder cluster.

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.

Abstract: A method, system and software program for automatically organizing digital images obtained from a plurality of hardcopy media. A plurality of hardcopy media are scanned so as to obtain both the image side and non-image side the of hardcopy media including capturing any watermark present on non-image side. The watermark on the non-mage side is used for automatically organizing digital images.

Abstract: A database has chronologically ordered images classified into event groups based upon a time difference threshold, and into subgroups based upon a similarity measure. In a method and system for combining new images into such a database, new images are ordered into clusters based upon assessed image features. A representative image is selected in each cluster. A database segment chronologically overlapping the new images is designated and a set of database images similar to each representative image are identified in the segment. Different subgroups including one or more retrieved images are associated with each cluster to provide matched subgroups. The new images are assigned to matched subgroups associated with respective clusters.

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: A method for associating event times or time periods with digital images in a collection for determining if a digital image is of interest, includes storing a collection of digital images each having an associated capture time; comparing the associated capture time in the collection with a special event time to determine if a digital image in the collection is of interest, wherein the comparing step includes calculation of a special event time associated with a special event based on the calendar time associated with the special event and using such information to perform the comparison step; and associating digital images of interest with the special event.

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 context-based concept fusion method detects a first concept in an image record. The method includes automatically determining at least one other concept in the image record which has a contextual relationship with the first concept and which is to be labeled by a user of the method; and labeling the at least one other concept by the user with a ground truth label to be used in the context-based concept fusion method to improve detection of the first concept in the image record.

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: 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.