Method and apparatus for organizing media content

Abstract

A method that incorporates teachings of the subject disclosure may include, for example, determining, by a system comprising a processor, more common features of a plurality of images according to similarity matrices indicating relative similarities between instances of common features occurring within multiple images of the plurality of images, defining, by the system, cluster groups associated with the more common features, where each cluster group comprises cluster images of the plurality of images, and where the more common features are present in each the cluster images, and performing, by the system, quality-based filtering on the cluster images to identify a target cluster image to represent the cluster images for each of the cluster groups. Other embodiments are disclosed.

What is claimed is: 
     
         1 . A device, comprising:
 a processing system including a processor; and   a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising:   identifying a plurality of features in a plurality of images;   selecting a plurality of common features from the plurality of features in the plurality of images;   based on similarity metrics indicating similarity between images of the plurality of images, determining a number of cluster groups to be defined for the plurality of images, to obtain a determined number of cluster groups, wherein the determining the number of cluster groups comprises evaluating each of a plurality of candidate cluster group values according to the similarity metrics;   defining a plurality of cluster groups comprising the determined number of cluster groups, wherein each cluster group comprises a plurality of cluster images, wherein each cluster group is associated with a common feature of the plurality of common features selected from the plurality of images, and wherein the plurality of cluster groups is defined according to a user interest that is associated with a portion of the plurality of images;   receiving a user selection of a representative image for a first cluster group of the plurality of cluster groups;   determining an interest level according to one or more objects captured in the representative image;   obtaining an additional image; and   based on the determining the interest level, adding the additional image to one of the plurality of cluster groups or creating another cluster group.   
     
     
         2 . The device of  claim 1 , wherein the determining the interest level is responsive to the receiving the user selection of the representative image. 
     
     
         3 . The device of  claim 1 , wherein the similarity metrics comprise scores representing similarities between two-dimensional features detected in different images of the plurality of images and/or similarities between three-dimensional features detected in different images of the plurality of images. 
     
     
         4 . The device of  claim 1 , wherein the creating the another cluster group comprises including the additional image in the another cluster group, identifying images in the plurality of images that have features similar to features in the additional image, or a combination thereof. 
     
     
         5 . The device of  claim 1 , wherein the operations further comprise:
 determining the representative image for the first cluster group.   
     
     
         6 . The device of  claim 5 , wherein the operations further comprise:
 filtering the plurality of cluster images in the first cluster group, wherein the determining the representative image for the first cluster group is based on the filtering.   
     
     
         7 . The device of  claim 6 , wherein the filtering comprises performing a no-reference analysis of the plurality of cluster images in the first cluster group. 
     
     
         8 . The device of  claim 6 , wherein the filtering comprises identifying image blur in the plurality of cluster images in the first cluster group. 
     
     
         9 . The device of  claim 6 , wherein the filtering comprises identifying compression artifacts in the plurality of cluster images in the first cluster group. 
     
     
         10 . The device of  claim 5 , wherein the operations further comprise:
 performing facial recognition on the plurality of cluster images in the first cluster group to identify a face in a frontal orientation, wherein the determining the representative image for the first cluster group is based on identifying the face in the frontal orientation.   
     
     
         11 . A non-transitory machine-readable storage medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, comprising:
 identifying a plurality of common features in a plurality of images;   based on similarity metrics indicating similarity between images of the plurality of images, defining a plurality of cluster groups for the plurality of images, wherein each cluster group of the plurality of cluster groups comprises a plurality of cluster images, wherein each cluster group of the plurality of cluster groups is associated with a common feature of the plurality of common features;   detecting a user selection of a representative image for a first cluster group of the plurality of cluster groups;   determining an interest level according to one or more objects captured in the representative image;   receiving an additional image; and   based on the determining the interest level, creating another cluster group.   
     
     
         12 . The non-transitory machine-readable storage medium of  claim 11 , wherein the determining the interest level is responsive to the receiving the user selection of the representative image. 
     
     
         13 . The non-transitory machine-readable storage medium of  claim 11 , wherein the creating the another cluster group comprises including the additional image in the another cluster group, identifying images in the plurality of images that have features similar to features in the additional image, or a combination thereof. 
     
     
         14 . The non-transitory machine-readable storage medium of  claim 11 , wherein the operations further comprise:
 determining the representative image for the first cluster group.   
     
     
         15 . The non-transitory machine-readable storage medium of  claim 14 , wherein the operations further comprise:
 filtering the plurality of cluster images in the first cluster group, wherein the determining the representative image for the first cluster group is based on the filtering.   
     
     
         16 . The non-transitory machine-readable storage medium of  claim 11 , wherein the filtering comprises performing a no-reference analysis of the plurality of cluster images in the first cluster group. 
     
     
         17 . A method, comprising:
 identifying, by a processing system including a processor, a plurality of features in a plurality of images;   selecting, by the processing system, a plurality of common features from the plurality of features in the plurality of images;   based on similarity metrics indicating similarity between images of the plurality of images, determining, by the processing system, a number of cluster groups to be defined for the plurality of images, to obtain a determined number of cluster groups, wherein the determining the number of cluster groups comprises evaluating each of a plurality of candidate cluster group values according to the similarity metrics;   defining, by the processing system, a plurality of cluster groups comprising the determined number of cluster groups, wherein each cluster group comprises a plurality of cluster images, wherein each cluster group is associated with a common feature of the plurality of common features selected from the plurality of images, and wherein the plurality of cluster groups is defined according to a user interest that is associated with a portion of the plurality of images;   identifying, by the processing system, a representative image for a first cluster group of the plurality of cluster groups, wherein identifying the representative image for the first cluster group comprises filtering images of the plurality of cluster images in the first cluster group based on a no-reference analysis, an image blur analysis, a compression artifacts analysis, or a combination thereof;   receiving, by the processing system, a user selection of the representative image for the first cluster group;   determining, by the processing system, an interest level according to one or more objects captured in the representative image;   obtaining, by the processing system, an additional image; and   based on the determining the interest level, adding, by the processing system, the additional image to one of the plurality of cluster groups or creating another cluster group.   
     
     
         18 . The method of  claim 17 , wherein the determining the interest level is responsive to the receiving the user selection of the representative image. 
     
     
         19 . The method of  claim 17 , wherein the creating the another cluster group comprises including the additional image in the another cluster group, identifying images in the plurality of images that have features similar to features in the additional image, or a combination thereof. 
     
     
         20 . The method of  claim 17 , wherein the similarity metrics comprise scores representing similarities between two-dimensional features detected in different images of the plurality of images and/or similarities between three-dimensional features detected in different images of the plurality of images.