A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic thumbnail cropping and its effectiveness
Proceedings of the 16th annual ACM symposium on User interface software and technology
MUM '05 Proceedings of the 4th international conference on Mobile and ubiquitous multimedia
Seam carving for content-aware image resizing
ACM SIGGRAPH 2007 papers
Self-Adaptive Image Cropping for Small Displays
IEEE Transactions on Consumer Electronics
Scale and Object Aware Image Thumbnailing
International Journal of Computer Vision
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We propose a model for automatically cropping images based on a diverse set of content and spatial features. We approach this by extracting pixel-level features and aggregating them over possible crop regions. We then learn a regression model to predict the quality of the crop regions, via the degree to which they would overlaps with human-provided crops from these input features. Candidate images can then be cropped based an exhaustive sweep over candidate crop regions, where each region is scored and the highest-scoring region is retained. The system is unique in its ability to incorporate a variety of pixel-level importance cues when arriving at a final cropping recommendation. We test the system on a set of human-cropped images with a large set of features. We find that the system outperforms baseline approaches, particularly when the aspect ratio of the image is very different from the target thumbnail region.