Techniques for automatic photo album generation

摘要

A computer-implemented technique can receive, at a computing device including one or more processors, a plurality of photos. The technique can extract quality features and similarity features for each of the plurality of photos and can obtain weights for the various quality features and similarity features based on an analysis of a reference photo collection. The technique can generate a quality metric for each of the plurality of photos and can generate a similarity matrix for the plurality of photos by analyzing the various quality features and similarity features and using the obtained weights. The technique can perform joint global maximization of photo quality and photo diversity using the quality metrics and the similarity matrix in order to select a subset of the plurality of photos having a high degree of representativeness. The technique can then store the subset of the plurality of photos in a memory.

主权项

1. A computer-implemented method comprising:
receiving, by a computing device including one or more processors, a plurality of photos; extracting, by the computing device, a set of quality features from each of the plurality of photos, the set of quality features including two or more features, wherein each feature of the set of quality features corresponds to a quality of a specific photo, and wherein the set of quality features includes at least one of photometric features, saliency-based features, and content-based features for a specific photo; extracting, by the computing device, a set of similarity features from each of the plurality of photos, each of the set of similarity features being indicative of a similarity between a specific photo and another one or more of the plurality of photos, the set of similarity features including at least one of spatial resolution, color resolution, and temporal resolution of the specific photo; obtaining, by the computing device, a quality weight for each feature of the set of quality features by performing machine learning on a reference photo collection using an L2 regularization with an L2-loss function to obtain a set of quality weights wherein the L2 regularization with an L2-loss function is a machine learning regularization function that uses a squared loss function, the reference photo collection including plurality of reference photos and a quality weight associated with each of the plurality of reference photos; obtaining, by the computing device, a similarity weight for each feature of the set of similarity features based on an analysis of the reference photo collection to obtain a set of similarity weights, the reference photo collection including a similarity weight associated with each unique pair of reference photos in the reference photo collection; generating, by the computing device, a quality metric for each of the plurality of photos by analyzing the set of quality features for a specific photo to obtain a set of quality scores and combining the set of quality scores using the set of quality weights to obtain the quality metric; generating, by the computing device, a similarity matrix for the plurality of photos by analyzing the set of similarity features for each unique pair of photos of the plurality of photos to obtain a set of similarity scores and generating the similarity matrix using the set of similarity scores and the set of similarity weights; selecting, by the computing device, a subset of the plurality of photos by performing joint global maximization of photo quality and photo diversity based on the quality metrics and the similarity matrix using a determinantal point process (DPP) including a maximum-a-posteriori (MAP) approximation algorithm to determine a number of iterations for performing the selection of the selected subset of photos; and storing by the computing device, the subset of the plurality of photos.