Abstract: A machine-learning based method includes receiving an instruction to model an engagement predicting score for a user. User-specific, activity-specific data is obtained from digital resources that include a user-specific activity performance data regarding performance of at least one activity by the user, an object data for an object that allows the user to perform the at least one activity, and user-specific personal data of the user. A user-specific activity engagement labeling data for the at least one activity is predicted by utilizing a first-type data pipeline on the at least one user-specific activity performance data. User-specific, activity-specific data features are predicted by utilizing a second-type data pipeline on the user-specific, activity-specific data. The engagement predicting score is predicted from the user-specific, activity-specific data features and the user-specific activity engagement labeling data.

Abstract: A machine-learning based method includes receiving an instruction to model an engagement predicting score for a user. User-specific, activity-specific data is obtained from digital resources that include a user-specific activity performance data regarding performance of at least one activity by the user, an object data for an object that allows the user to perform the at least one activity, and user-specific personal data of the user. A user-specific activity engagement labeling data for the at least one activity is predicted by utilizing a first-type data pipeline on the at least one user-specific activity performance data. User-specific, activity-specific data features are predicted by utilizing a second-type data pipeline on the user-specific, activity-specific data. The engagement predicting score is predicted from the user-specific, activity-specific data features and the user-specific activity engagement labeling data.

Abstract: A machine-learning based method includes receiving an instruction to model an engagement predicting score for a user. User-specific, activity-specific data is obtained from digital resources that include a user-specific activity performance data regarding performance of at least one activity by the user, an object data for an object that allows the user to perform the at least one activity, and user-specific personal data of the user. A user-specific activity engagement labeling data for the at least one activity is predicted by utilizing a first-type data pipeline on the at least one user-specific activity performance data. User-specific, activity-specific data features are predicted by utilizing a second-type data pipeline on the user-specific, activity-specific data. The engagement predicting score is predicted from the user-specific, activity-specific data features and the user-specific activity engagement labeling data.

Abstract: A machine-learning based method includes receiving an instruction to model an engagement predicting score for a user. User-specific, activity-specific data is obtained from digital resources that include a user-specific activity performance data regarding performance of at least one activity by the user, an object data for an object that allows the user to perform the at least one activity, and user-specific personal data of the user. A user-specific activity engagement labeling data for the at least one activity is predicted by utilizing a first-type data pipeline on the at least one user-specific activity performance data. User-specific, activity-specific data features are predicted by utilizing a second-type data pipeline on the user-specific, activity-specific data. The engagement predicting score is predicted from the user-specific, activity-specific data features and the user-specific activity engagement labeling data.

Abstract: This disclosure provides method and systems of classifying a digital image of an object. Specifically, according to one exemplary embodiment, an object classifier is trained using a constrained MI-SVM (multiple instance-support vector machine) approach whereby training images of objects are sampled to generate a collection of image regions associated with an object type and viewpoint, and the classifier is trained to determine an appropriate mid-level representation of the training image which is discriminative.

Abstract: A system and method for searching a finite collection of images using at least one semantic network. Upon receipt of a query from a user that includes a theme and one or more initial keywords, a set of keywords based on the theme and including the initial keywords is generated from one or more semantic networks corresponding to the theme and/or initial keywords. When the finite collection of images includes suitable metadata, a result set is generated of images corresponding to the expanded set of keywords. When the finite collection includes images lacking in metadata, a remote third-party image collection is searched with the set of keywords to obtain a result set that is used to train visual classifiers as to visual concepts associated with the keywords. The classifiers are used to classify the images in the finite collection lacking metadata and the search of the finite collection is performed with the set of keywords to generate a result set.

Abstract: A method, a system, and a computer program product for extracting one or more images from a storage medium. A search model is selected based on the availability of a semantically related aesthetic model. A search model includes a generic aesthetic model if the semantically related aesthetic model for query is not available. A semantic score and an aesthetic score are computed based on the selected search model. The images are further ranked based on the semantic and aesthetic score.

Abstract: The disclosed embodiments related to a method and system for creating a digital image album implementable on a computing device. The method includes grouping a plurality of digital images to generate a plurality of groups. Each of the plurality of groups is then transmitted to one or more crowdworkers for ranking each digital image of the plurality of groups. A final rank corresponding to each of the plurality of digital images is then determined based on the one or more ranks assigned by the one or more crowdworkers to each digital image of the plurality of groups. Thereafter, at least one digital image is selected from each of the plurality of groups based on the final rank of at least one digital image to create the digital image album.

Abstract: This disclosure provides method and systems of classifying a digital image of an object. Specifically, according to one exemplary embodiment, an object classifier is trained using a constrained MI-SVM (multiple instance-support vector machine) approach whereby training images of objects are sampled to generate a collection of image regions associated with an object type and viewpoint, and the classifier is trained to determine an appropriate mid-level representation of the training image which is discriminative.

Abstract: An image selection method includes receiving a collection of images and optionally, filtering the collection of images. The filtering may include removing images that are near-duplicates of other images in the collection. A plurality of features is extracted from each of the images in the optionally-filtered collection. The optionally-filtered collection of images is considered as a time-ordered sequence of images and is segmented to form a sequence of segments. Each segment includes at least one of the images. The segmenting of the sequence of images is based on the extracted features and positions of the images in the sequence of images. Images from the segments are selected to form a sub-collection of the images.

Abstract: The disclosed embodiments are related to a method and system for creating a digital image album implementable on a computing device. The method includes receiving a plurality of digital images. The method further includes creating a first signature corresponding to each of the plurality of digital images. The method further includes comparing the first signature corresponding to each of the plurality of digital images with one or more second signatures. Each of the second signatures corresponds to each of one or more prototype digital albums. The method further includes selecting one or more digital images from the plurality of digital images based on the comparison to create the digital image album.

Abstract: A method for learning visual attribute labels for images includes, from textual comments associated with a corpus of images, identifying a set of candidate textual labels that are predictive of aesthetic scores associated with images in the corpus. The candidate labels in the set are clustered into a plurality of visual attribute clusters based on similarity and each of the clusters assigned a visual attribute label. For each of the visual attribute labels, a classifier is trained using visual representations of images in the corpus and respective visual attribute labels. The visual attribute labels are evaluated, based on performance of the trained classifier. A subset of the visual attribute labels is retained, based on the evaluation. The visual attribute labels can be used in processes such as image retrieval, image labeling, and the like.

Abstract: A method for learning visual attribute labels for images includes, from textual comments associated with a corpus of images, identifying a set of candidate textual labels that are predictive of aesthetic scores associated with images in the corpus. The candidate labels in the set are clustered into a plurality of visual attribute clusters based on similarity and each of the clusters assigned a visual attribute label. For each of the visual attribute labels, a classifier is trained using visual representations of images in the corpus and respective visual attribute labels. The visual attribute labels are evaluated, based on performance of the trained classifier. A subset of the visual attribute labels is retained, based on the evaluation. The visual attribute labels can be used in processes such as image retrieval, image labeling, and the like.

Abstract: The disclosed embodiments are related to a method and system for creating a digital image album implementable on a computing device. The method includes receiving a plurality of digital images. The method further includes creating a first signature corresponding to each of the plurality of digital images. The method further includes comparing the first signature corresponding to each of the plurality of digital images with one or more second signatures. Each of the second signatures corresponds to each of one or more prototype digital albums. The method further includes selecting one or more digital images from the plurality of digital images based on the comparison to create the digital image album.

Abstract: A system and method for searching a finite collection of images using at least one semantic network. Upon receipt of a query from a user that includes a theme and one or more initial keywords, a set of keywords based on the theme and including the initial keywords is generated from one or more semantic networks corresponding to the theme and/or initial keywords. When the finite collection of images includes suitable metadata, a result set is generated of images corresponding to the expanded set of keywords. When the finite collection includes images lacking in metadata, a remote third-party image collection is searched with the set of keywords to obtain a result set that is used to train visual classifiers as to visual concepts associated with the keywords. The classifiers are used to classify the images in the finite collection lacking metadata and the search of the finite collection is performed with the set of keywords to generate a result set.

Abstract: A method, a system, and a computer program product for extracting one or more images from a storage medium. A search model is selected based on the availability of a semantically related aesthetic model. A search model includes a generic aesthetic model if the semantically related aesthetic model for query is not available. A semantic score and an aesthetic score are computed based on the selected search model. The images are further ranked based on the semantic and aesthetic score.

Abstract: A system and method are disclosed for image selection based on photographic style in which photographic style annotations are learned using a data-driven approach. The method includes assigning a style value for each of a set of photographic style categories to each of a set of database images with a trained classifier of a computing device. A user's selection of a subset of the photographic style categories, such as three style categories, is received. A user interface is generated for assigning values to each of the selected photographic style categories. A set of database images is identified, based on the assigned values for each of the selected photographic style categories and the style values for each of the selected photographic style categories of the database images.

Abstract: An image selection method includes receiving a collection of images and optionally, filtering the collection of images. The filtering may include removing images that are near-duplicates of other images in the collection. A plurality of features is extracted from each of the images in the optionally-filtered collection. The optionally-filtered collection of images is considered as a time-ordered sequence of images and is segmented to form a sequence of segments. Each segment includes at least one of the images. The segmenting of the sequence of images is based on the extracted features and positions of the images in the sequence of images. Images from the segments are selected to form a sub-collection of the images.

Abstract: A system and method for assisting a user in navigation of an image dataset are disclosed. The method includes receiving a user's text query, retrieving images responsive to the query from an image dataset, providing for receiving the user's selection of a first feature selected from a set of available first features via a graphical user interface, providing for receiving the user's selection of a second feature selected from a set of available second features different from the first features via the graphical user interface, and displaying at least some of the retrieved images on the graphical user interface. The displayed images are arranged, e.g., grouped, according to levels and/or combinations of levels of the user-selected first and second features.

Abstract: Systems and methods of replacing digital assets within a multimedia document are provided. The systems and methods include a user workstation that can receive a selection from a user for an original asset in the document to be replaced. Alternative assets can be retrieved that have a level of appropriateness with the selected original asset. Constraints on use of the alternative assets can be determined and a fitness value of each of the alternative assets can be calculated based on the appropriateness and the constraints on use. The alternative assets with the highest fitness values can be presented to the user for the user to select to replace the original asset.