Abstract: Methods, systems, and computer readable media with executable instructions, and/or logic are provided for incremental image clustering. An example method for incremental image clustering can include identifying, via a computing device, a number of candidate nodes from among evaluated leaf image cluster (LIC) nodes on an image cluster tree (ICT) based on a similarity between a feature of a new image and an average feature of each of the evaluated LIC nodes. The evaluated nodes include at least one node along each path from a root node to either a leaf node or a node having a similarity exceeding a first threshold. A most-similar node can be determined, via the computing device, from among the number of candidate nodes. The new image can be inserted to a node associated with the determined most-similar node, via the computing device.

Abstract: Examples of the present disclosure may include methods, systems, and computer readable media with executable instructions. An example method for retrieving visual media can include receiving a text query associated with a target content. A first group of visual media is identified based on correspondence of metadata of the visual media with the text query, and keyframes from the first group of identified visual media are selected. The method further includes detecting instances of a content type in the selected keyframes, and grouping similar instances of the content type into clusters. The target content is associated with a cluster having a greatest quantity of similar instances.

Abstract: A method for tracking an object in a video data, comprises the steps of determining a plurality of particles for estimating a location of the object in the video data, determining a weight for each of the plurality of the particles, wherein the weights of two or more particles are determined substantially in parallel, and estimating the location of the object in the video data based upon the determined particle weights.

Abstract: Examples of the present disclosure may include methods, systems, and computer readable media with executable instructions. An example method for retrieving visual media can include receiving a text query associated with a target content. A first group of visual media is identified based on correspondence of metadata of the visual media with the text query, and keyframes from the first group of identified visual media are selected. The method further includes detecting instances of a content type in the selected keyframes, and grouping similar instances of the content type into clusters. The target content is associated with a cluster having a greatest quantity of similar instances.

Abstract: Methods, systems, and computer readable media with executable instructions, and/or logic are provided for incremental image clustering. An example method for incremental image clustering can include identifying, via a computing device, a number of candidate nodes from among evaluated leaf image cluster (LIC) nodes on an image cluster tree (ICT) based on a similarity between a feature of a new image and an average feature of each of the evaluated LIC nodes. The evaluated nodes include at least one node along each path from a root node to either a leaf node or a node having a similarity exceeding a first threshold. A most-similar node can be determined, via the computing device, from among the number of candidate nodes. The new image can be inserted to a node associated with the determined most-similar node, via the computing device.

Abstract: Embodiments of the present invention relate to a data processing method comprising the steps of processing a first sample set associated with first results of tracking a target in a number of image frames using a first object classifier arranged to operate according to a first feature and processing a second sample set associated with second results of tracking the target in said number of image frames using a second object classifier arrange to operate according to a second feature, and using at least one of the first and second tracking results to influence at least one particle filter adapted to influence at least one of subsequent first and second tracking results.

Abstract: Embodiments of the present invention relate to a data processing method comprising the steps of processing a first sample set associated with first results of tracking a target in a number of image frames using a first object classifier arranged to operate according to a first feature and processing a second sample set associated with second results of tracking the target in said number of image frames using a second object classifier arrange to operate according to a second feature, and using at least one of the first and second tracking results to influence at least one particle filter adapted to influence at least one of subsequent first and second tracking results.

Abstract: A method for tracking an object in a video data, comprises the steps of determining a plurality of particles for estimating a location of the object in the video data, determining a weight for each of the plurality of the particles, wherein the weights of two or more particles are determined substantially in parallel, and estimating the location of the object in the video data based upon the determined particle weights.