Abstract: Labels for unlabeled media samples may be determined automatically. Characteristics and/or features of an unlabeled media sample are detected and used to iteratively optimize a distance metric and one or more labels for the unlabeled media sample according to an algorithm. The labels may be used to produce training data for a machine learning process.

Abstract: Systems and methods for template-based multimedia authoring and sharing are described. In one aspect, media content is selectively applied to a content description template to author media in a content description. The content description template provides a temporal structure for the applied media content. A content representation template is selected and combined with the temporally structured media in the content description to specify rendering criteria and generate a content description and representation for one or more of rendering, sharing, and exporting the temporally structured authored media.

Abstract: Methods and systems for object-sensitive image searches are described herein. These methods and systems are usable for receiving a query for an image of an object and providing a ranked list of query results to the user based on a ranking of the images. The object-sensitive image searches may generate a pre-trained multi-instance learning (MIL) model trained from free training data from users sharing images at websites to identify a common pattern of the object, and/or may generate a MIL model “on the fly” trained from pseudo-positive and pseudo-negative samples of query results to identify a common pattern of the object. As such, the user is presented with query results that include images that prominently display the object near the top of the results.

Abstract: Exemplary media browsing, searching and authoring tools allow for media interaction over a web. An exemplary method includes acquiring digital video data, coding the digital video data using scalable video coding to generate scalable coded digital video data, analyzing the scalable coded digital video data using one or more video filters to generate information pertaining to the scalable coded digital video data and providing web access to the information. Various other exemplary technologies are disclosed.

Abstract: A general framework for video search reranking is disclosed which explicitly formulates reranking into a global optimization problem from the Bayesian perspective. Under this framework, with two novel pair-wise ranking distances, two effective video search reranking methods, hinge reranking and preference strength reranking, are disclosed. Experiments conducted on the TRECVID dataset have demonstrated that the disclosed methods outperform several existing reranking approaches.

Abstract: Techniques for constructing an optimized kd-tree are described. In an implementation, an optimized kd-tree process receives input of a set of data points applicable for large-scale computer vision applications. The process divides the set of the data points into subsets of data points with nodes while generating hyperplanes (e.g., coordinate axes). The process identifies a partition axis for each node based on the coordinate axes combined in a binary way. The optimized kd-tree process creates an optimized kd-tree that organizes the data points based on the identified partition axis. The organization of the data points in the optimized kd-tree provides efficient indexing and searching for a nearest neighbor.

Abstract: Technologies for generating a boosted tag ranking for a media instance, the boosted tag ranking based on probabilistic relevance estimation computed by a probabilistic relevance estimator and tag correlation refining performed by a tag correlation refiner. Such boosted tag rankings may be used for search result ranking, tag recommendation, and group recommendation.

Abstract: Techniques for the design and operation of a blogging tool for automated blog creation and automated upload to a server are described herein. A content capturing process may obtain a plurality of images, including still images or video, as well as audio capture of voices and other sound, according to direction of a user operating an image-capture device. One or more of the images may be annotated with metadata or with text, which may be derived from verbal content provided by the user. A template may be selected in either an automated or user-controlled manner. The images and other content may be assembled into the template to form a blog entry. The blog entry may be uploaded to a server or otherwise shared. In one example, the uploading may be in response to a single user command, obtained by operation of a physical user interface or from verbal user input.

Abstract: The sponsored multi-media blogging technique is an advertising-driven service on a computing device, such as a mobile phone, that makes the multi-media micro-blog or blog an effective carrier for advertising. The data collected while employing the sponsored multi-media blogging technique is used for user intent mining and increasing advertisement relevance for mobile advertising projects. The benefits to the sponsored multi-media blogging technique's users are a natural interface for composing multi-media micro-blogs/blogs and instant experience sharing, while the benefits to advertisers is the promoted brand impression from the contextual advertising in rich media micro-blogs/blogs.

Abstract: An adaptation process is described to adapt a ranking model constructed for a broad-based search engine for use with a domain-specific ranking model. An example process identifies a ranking model for use with a broad-based search engine and modifies that ranking model for use with a new (or “target”) domain containing information pertaining to a specific topic.

Abstract: Techniques for image selection and region of interest analysis are described herein. A pair of two or more users is configured, and an image is displayed to the pair. The image can be a still image (i.e., a picture) or a moving image (i.e., video). In some instances, a plurality of advertisements is suggested for possible association with the image. Input is received from both users in the pair, indicating a positive or a negative association between each advertisement and the image. When the pair positively rates an advertisement, the advertisement is associated with the image. A plurality of regions of interest within the image may be suggested. In response, positive or negative input is received from the pair indicating whether each of the plurality of regions of interest is appropriately suggested for placement of an advertisement.

Abstract: Region-based image manipulation can include selecting and segmenting regions of a particular image. The regions are identified through the use of simplified brushstrokes over pixels of the regions. Identified regions can be manipulated or transformed accordingly. Certain implementations include filling in regions with other images or objects, and include performing a text query to search for such images or objects.

Abstract: Kernelized spatial-contextual image classification is disclosed. One embodiment comprises generating a first spatial-contextual model to represent a first image, the first spatial-contextual model having a plurality of interconnected nodes arranged in a first pattern of connections with each node connected to at least one other node, generating a second spatial-contextual model to represent a second image using the first pattern of connections, and estimating the distance between corresponding nodes in the first spatial-contextual model and the second spatial-contextual model based on a relationship with adjacent connected nodes to determine a distance between the first image and the second image.

Abstract: Multi-label active learning may entail training a classifier with a set of training samples having multiple labels per sample. In an example embodiment, a method includes accepting a set of training samples, with the set of training samples having multiple respective samples that are each respectively associated with multiple labels. The set of training samples is analyzed to select a sample-label pair responsive to at least one error parameter. The selected sample-label pair is then submitted to an oracle for labeling.

Abstract: A color indication tool is described that enables a colorblind user to better perceive and recognize visual documents. An exemplary process utilizes a user-input device, such as a mouse or a stylus, to identify a pixel, region, object within an image. The color indication tool provides an indication of the color of the identified pixel, region or object.

Abstract: Methods and systems for active image tagging are usable to build large datasets of tagged images by combining manual tagging by a user and automatic tagging by a computing device based on the manual tagging. Such tags may be used to effectively sort, organize, link, and search for images within large datasets of images. Additionally, the active image tagging may be configured to utilize a tagging game where multiple users manually tag images by playing a game on a computing device.

Abstract: Colors of images and videos are modified to make differences in the colors more perceptible to colorblind users. An exemplary recoloring process utilizes a color space transformation, a local color rotation and a global color rotation to transform colors of visual objects from colors which may not be distinguishable by the colorblind user to colors which may be distinguishable by the colorblind user.

Abstract: Techniques for recommending music and advertising to enhance a user's experience while photo browsing are described. In some instances, songs and ads are ranked for relevance to at least one photo from a photo album. The songs, ads and photo(s) from the photo album are then mapped to a style and mood ontology to obtain vector-based representations. The vector-based representations can include real valued terms, each term associated with a human condition defined by the ontology. A re-ranking process generates a relevancy term for each song and each ad indicating relevancy to the photo album. The relevancy terms can be calculated by summing weighted terms from the ranking and the mapping. Recommended music and ads may then be provided to a user, as the user browses a series of photos obtained from the photo album. The ads may be seamlessly embedded into the music in a nonintrusive manner.

Abstract: Users may browse web pages, interact with a plethora of applications, search for new content, and perform a wide variety of other tasks using a mobile device. Unfortunately, useful content may be difficult for a user to locate because of the large amount of content available (e.g. hundreds of thousands of applications within an application store). Accordingly, one or more systems and/or techniques for determining recommendations are disclosed herein. In particular, user input (e.g., text, numbers, etc.) and/or a user profile (e.g., contextual information relating to a user) may be used to determine a user intent. Recommendations may be determined based upon the user intent. For example, a user may input “I am hungry” using a mobile phone having a GPS location of Downtown and a noon timestamp. Using this information, an application allowing the user to make lunch reservations at local restaurants may be provided as a recommendation.

Abstract: Described is perceptually near-lossless video summarization for use in maintaining video summaries, which operates to substantially reconstruct an original video in a generally perceptually near-lossless manner. A video stream is summarized with little information loss by using a relatively very small piece of summary metadata. The summary metadata comprises an image set of synthesized mosaics and representative keyframes, audio data, and the metadata about video structure and motion. In one implementation, the metadata is computed and maintained (e.g., as a file) to summarize a relatively large video sequence, by segmenting a video shot into subshots, and selecting keyframes and mosaics based upon motion data corresponding to those subshots. The motion data is maintained as a semantic description associated with the image set.