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: 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.