A physical model-based approach to detecting sky in photographic images

Sky is among the most important subject matter frequently seen in photographic images. We propose a model-based approach consisting of color classification, region extraction, and physics-motivated sky signature validation. First, the color classification is performed by a multilayer backpropagation...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 11; no. 3; pp. 201 - 212
Main Authors: Jiebo Luo, Etz, S.P.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.03.2002
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Backpropagation algorithms
Cathode ray tubes
Classification
Color
Computer science; control theory; systems
Content based retrieval
Exact sciences and technology
Extraction
Image processing
Image recognition
Image retrieval
Information, signal and communications theory
Laboratories
Layout
Multi-layer neural network
Multilayers
Neural networks
Pattern recognition. Digital image processing. Computational geometry
Physics computing
Signal processing
Signatures
Sky
Studies
Telecommunications and information theory
Sky
Backpropagation
Photographic image
Image processing
Neural network
Algorithm
Image segmentation
Physical model
Object detection
Feature extraction
Gradient method
Desaturation
Image classification
ISSN:1057-7149, 1941-0042
Online Access:Get full text
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Summary:Sky is among the most important subject matter frequently seen in photographic images. We propose a model-based approach consisting of color classification, region extraction, and physics-motivated sky signature validation. First, the color classification is performed by a multilayer backpropagation neural network trained in a bootstrapping fashion to generate a belief map of sky color. Next, the region extraction algorithm automatically determines an appropriate threshold for the sky color belief map and extracts connected components. Finally, the sky signature validation algorithm determines the orientation of a candidate sky region, classifies one-dimensional (1-D) traces within the region based on a physics-motivated model, and computes the sky belief of the region by the percentage of traces that fit the physics-based sky trace model. A small-scale, yet rigorous test has been conducted to evaluate the algorithm performance. With approximately half of the images containing blue sky regions, the detection rate is 96% with a false positive rate of 2% on a per image basis.
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ISSN:1057-7149
1941-0042
DOI:10.1109/83.988954