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CVPR
2004
IEEE

A Probabilistic Approach to Image Orientation Detection via Confidence-Based Integration of Low-Level and Semantic Cues

13 years 6 months ago
A Probabilistic Approach to Image Orientation Detection via Confidence-Based Integration of Low-Level and Semantic Cues
Automatic image orientation detection for natural images is a useful, yet challenging research area. Humans use scene context and semantic object recognition to identify the correct image orientation. However, it is difficult for a computer to perform the task in the same way because current object recognition algorithms are extremely limited in their scope and robustness. As a result, existing orientation detection methods were built upon lowlevel vision features such as spatial distributions of color and texture. In addition, discrepant detection rates have been reported. We have developed a probabilistic approach to image orientation detection via confidence-based integration of low-level and semantic cues within a Bayesian framework. Our current accuracy is approaching 90% for unconstrained consumer photos, impressive given the findings of a psychophysical study conducted recently. The proposed framework is an attempt to bridge the gap between computer and human vision systems, an...
Jiebo Luo, Matthew R. Boutell
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2004
Where CVPR
Authors Jiebo Luo, Matthew R. Boutell
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