# US 2014/0193048 A1 · Retrieving Visual Media > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2014/0193048 A1 - Document ID: US20140193048A1 - Status: Published application - Kind: A1 - Application number: 14/239,387 - Filed: 2011-09-27 - Published: 2014-07-10 - Earliest claimed: 2011-09-27 - Assignees: Hewlett-Packard Development Company, L.P. - Inventors: Tong Zhang; Ke-Yan Liu; Xin-Yun Sun - Web page: https://patentgrep.com/patent/US20140193048A1 ## CPC - G06F 16/5866: {using information manually generated, e.g. tags, keywords, comments, manually generated location and time information} - G06F 16/784: {the detected or recognised objects being people} - G06F 16/7844: {using original textual content or text extracted from visual content or transcript of audio data} - G06V 40/172: {Classification, e.g. identification} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## 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. ## Claims ### Claim 1 A method for retrieving visual media, comprising: receiving, using a processor, a text query associated with a target content identifying, using the processor, a first group of visual media based on correspondence of metadata of the visual media with the text query; selecting, using the processor, keyframes from the first group of identified visual media; detecting, using the processor, instances of a content type in the selected keyframes; grouping, using the processor, similar instances of the content type into clusters; and associating, using the processor, the target content with a cluster having a greatest quantity of similar instances. ### Claim 2 (depends on 1) The method of claim 1, wherein receiving, using the processor, a text query associated with a target content includes receiving a name, the target content being one or more images of a person with the name; and detecting, using the processor, instances of a content type in the selected keyframes includes detecting face images. ### Claim 3 (depends on 1) The method of claim 1, further comprising: selecting, using the processor, instances of a content type from each cluster having a threshold quantity of similar instances; and displaying, using the processor, the instances of a content type for each of the clusters having the threshold quantity of similar instances in an order corresponding to cluster size, wherein cluster size corresponds to quantity of similar instance of the content type in the cluster. ### Claim 4 (depends on 3) The method of claim 3, wherein selecting and displaying the instances of a content type for each of the clusters includes respectively listing segments within one or more video clips at which the target content appears. ### Claim 5 (depends on 4) The method of claim 4, wherein displaying images of faces appearing in one or more video clips includes displaying a largest face image and displaying a face image most different from a selected face image. ### Claim 6 (depends on 3) The method of claim 3, further comprising: receiving, using the processor, a user selection of at least one of the displayed instances: identifying, using the processor, a second group of visual media based on correspondence of metadata of the visual media with the text query and the selected instances of a content type; selecting, using the processor, second keyframes from the second group of identified visual media; detecting, using the processor, second instances of a content type in the selected second keyframes; grouping, using the processor, similar second instances of he content type into clusters; and determining, using the processor, matches between the second instances of the content type and the selected instances of a content type. ### Claim 7 (depends on 6) The method of claim 6, further comprising: determining, using the processor, a ranking score for visual media having at least one determined match based on cumulative time during which the selected instances of a content type appear; and displaying, using the processor, a listing of the visual media based on ranking score. ### Claim 8 (depends on 7) The method of claim 7, further comprising creating, using the processor, an index of the visual media based on ranking score and a quantity of occurrences that the visual media is viewed, the index including a location of the visual media and one or more locations within the visual media at which a particular instance of a content type appears. ### Claim 9 (depends on 7) The method of claim 7, further comprising: analyzing, using the processor, metadata of most-viewed visual media and a name associated with each respective most-viewed visual media prior to receiving the text query; generating, using the processor, a re-ranked result corresponding to the name; caching, using the processor, the re-ranked result; and updating, using the processor, the cached re-ranked result responsive to new text queries. ### Claim 10 (depends on 1) The method of claim 1, further comprising indexing, using the processor, locations within the visual media based on content type in the selected keyframes. ### Claim 11 (depends on 10) The method of claim 10, further comprising repurposing , using the processor, images from the indexed locations within the visual media onto customized image arrangements. ### Claim 12 A non-transitory computer-readable medium having computer-executable instructions stored thereon, the computer-executable instructions comprising instructions that, if executed by one or more processors, cause the one or more processors to: retrieve a group of visual media based on correspondence of metadata of the visual media with a text query; select keyframes from the group of retrieved visual media; apply face clustering to the keyframes from the group of retrieved visual media; generate query face images based on the face cluster and re-rank the group of retrieved visual media on a display based on a received input corresponding to a particular one of the query face images. ### Claim 13 (depends on 12) The non-transitory computer-readable medium of claim 12, further comprising instructions that, if executed by one or more processors. cause the one or more processors to indicate portions of a selected one of the group of retrieved visual media corresponding to a selected query face image. ### Claim 14 A computing system, comprising: a display; a non-transitory computer-readable medium having computer-executable instructions stored thereon; and a processor coupled to the display and the non-transitory computer-readable medium, wherein the computer-executable instructions comprise instructions that, if executed by the processor, cause the processor to: retrieve a group of visual media based on correspondence of metadata of the visual media with a text query; select keyframes from the group of retrieved visual media; apply face clustering to the keyframes from the group of retrieved visual media; generate query face images based on face clusters; and re-rank the group of retrieved visual media on the display based on a received input corresponding to a particular one of the query face images. ### Claim 15 (depends on 11) The computing system of claim 11, wherein the processor executes the instructions to display portions of a particular retrieved visual media based on a received input corresponding to a particular one of the query face images.