Abstract: Systems and methods for improving automatic selection of keeper images from a commonly captured set of images are described. A combination of image type identification and image quality metrics may be used to identify one or more images in the set as keeper images. Image type identification may be used to categorize the captured images into, for example, three or more categories. The categories may include portrait, action, or “other.” Depending on the category identified, the images may be analyzed differently to identify keeper images. For portrait images, an operation may be used to identify the best set of faces. For action images, the set may be divided into sections such that keeper images selected from each section tell the story of the action. For the “other” category, the images may be analyzed such that those having higher quality metrics for an identified region of interest are selected.

Abstract: Systems and methods for improving automatic selection of keeper images from a commonly captured set of images are described. A combination of image type identification and image quality metrics may be used to identify one or more images in the set as keeper images. Image type identification may be used to categorize the captured images into, for example, three or more categories. The categories may include portrait, action, or “other.” Depending on the category identified, the images may be analyzed differently to identify keeper images. For portrait images, an operation may be used to identify the best set of faces. For action images, the set may be divided into sections such that keeper images selected from each section tell the story of the action. For the “other” category, the images may be analyzed such that those having higher quality metrics for an identified region of interest are selected.

Abstract: Systems and methods for improving automatic selection of keeper images from a commonly captured set of images are described. A combination of image type identification and image quality metrics may be used to identify one or more images in the set as keeper images. Image type identification may be used to categorize the captured images into, for example, three or more categories. The categories may include portrait, action, or “other.” Depending on the category identified, the images may be analyzed differently to identify keeper images. For portrait images, an operation may be used to identify the best set of faces. For action images, the set may be divided into sections such that keeper images selected from each section tell the story of the action. For the “other” category, the images may be analyzed such that those having higher quality metrics for an identified region of interest are selected.

Abstract: The invention indexes an audiovisual recording to enable navigation within the audiovisual recording. In particular, the invention provides indexing within a “show” of a recording of an audiovisual broadcast (e.g., a television broadcast or a radio/satellite audio broadcast) that enables navigation within the show, in contrast to indexing among shows (i.e., identification of locations corresponding to the start and/or end of shows) that can enable navigation between shows. The invention also enables navigation among one or more intelligently selected segments of the recording in a non-linear manner. A graphical user interface can be implemented to effect navigation in accordance with the invention of a recording of audiovisual content.

Abstract: Process-response statistical modeling of visual images can be used in determining similarity between visual images. Evaluation of the content of visual images—and, in particular, image similarity determinations—can be used in effecting a variety of interactions (e.g., searching, indexing, grouping, summarizing, annotating, keyframing) with a collection of visual images.

Abstract: Process-response statistical modeling of visual images can be used in determining similarity between visual images. Evaluation of the content of visual images—and, in particular, image similarity determinations—can be used in effecting a variety of interactions (e.g., searching, indexing, grouping, summarizing, annotating, keyframing) with a collection of visual images.

Abstract: Process-response statistical modeling of visual images can be used in determining similarity between visual images. Evaluation of the content of visual images—and, in particular, image similarity determinations—can be used in effecting a variety of interactions (e.g., searching, indexing, grouping, summarizing, annotating, keyframing) with a collection of visual images.

Abstract: Process-response statistical modeling of visual images can be used in determining similarity between visual images. Evaluation of the content of visual images—and, in particular, image similarity determinations—can be used in effecting a variety of interactions (e.g., searching, indexing, grouping, summarizing, annotating, keyframing) with a collection of visual images.

Abstract: Process-response statistical modeling of visual images can be used in determining similarity between visual images. Evaluation of the content of visual images—and, in particular, image similarity determinations—can be used in effecting a variety of interactions (e.g., searching, indexing, grouping, summarizing, annotating, keyframing) with a collection of visual images.

Abstract: Process-response statistical modeling of visual images can be used in determining similarity between visual images. Evaluation of the content of visual images—and, in particular, image similarity determinations—can be used in effecting a variety of interactions (e.g., indexing, grouping, summarizing, annotating, searching keyframing) with a collection of visual images.