Communications of the ACM
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Studying aesthetics in photographic images using a computational approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Secure spread spectrum watermarking for multimedia
IEEE Transactions on Image Processing
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Emotion related structures in large image databases
Proceedings of the ACM International Conference on Image and Video Retrieval
A framework for photo-quality assessment and enhancement based on visual aesthetics
Proceedings of the international conference on Multimedia
Semantic analysis and retrieval in personal and social photo collections
Multimedia Tools and Applications
Employing aesthetic principles for automatic photo book layout
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Content based detection of popular images in large image databases
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
A holistic approach to aesthetic enhancement of photographs
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
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While personal and community-based image collections grow by the day, the demand for novel photo management capabilities grows with it. Recent research has shown that it is possible to learn the consensus on visual quality measures such as aesthetics with a moderate degree of success. Here, we seek to push this performance to more realistic levels and use it to (a) help select high-quality pictures from collections, and (b) eliminate low-quality ones, introducing appropriate performance metrics in each case. To achieve this, we propose a sequential arrangement of a weighted linear least squares regressor and a naive Bayes' classifier, applied to a set of visual features previously found useful for quality prediction. Experiments on real-world data for these tasks show promising performance, with significant improvements over a previously proposed SVM-based method.