Control of selective perception using Bayes nets and decision theory
International Journal of Computer Vision - Special issue on active vision II
Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Occlusion Models for Natural Images: A Statistical Study of a Scale-Invariant Dead Leaves Model
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
On Advances in Statistical Modeling of Natural Images
Journal of Mathematical Imaging and Vision
Depth Estimation from Image Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contextual Priming for Object Detection
International Journal of Computer Vision
Natural Image Statistics for Natural Image Segmentation
International Journal of Computer Vision
Semantic Modeling of Natural Scenes for Content-Based Image Retrieval
International Journal of Computer Vision
Streetscenes: towards scene understanding in still images
Streetscenes: towards scene understanding in still images
Recovering Surface Layout from an Image
International Journal of Computer Vision
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Beyond Nouns: Exploiting Prepositions and Comparative Adjectives for Learning Visual Classifiers
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Learning Spatial Context: Using Stuff to Find Things
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and incorporating top-down cues in image segmentation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Multiple region categorization for scenery images
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
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This work focuses on characterizing scenery images. We semantically divide the objects in natural landscape scenes into background and foreground and show that the shapes of the regions associated with these two types are statistically different. We then focus on the background regions. We study statistical properties such as size and shape, location and relative location, the characteristics of the boundary curves and the correlation of the properties to the region's semantic identity. Then we discuss the imaging process of a simplified 3D scene model and show how it explains the empirical observations. We further show that the observed properties suffice to characterize the gist of scenery images, propose a generative parametric graphical model, and use it to learn and generate semantic sketches of new images, which indeed look like those associated with natural scenery.