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...

• Type: Conference
• Year: 2004
• Where: CVPR
• Authors: Jiebo Luo, Matthew R. Boutell
• Keywords: Computer Vision, CVPR 2004, Image Orientation, Image Orientation Detection, Orientation Detection