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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jiebo Luo Singhal, A. Weiyu Zhu |
| Copyright Year | 2003 |
| Description | Author affiliation: Eastman Kodak Co., Oakdale, MN, USA (Jiebo Luo; Singhal, A.) |
| Abstract | Natural object detection in outdoor scenes, i.e., identifying key object types such as sky, grass, foliage, water, and snow, can facilitate content-based applications, ranging from image enhancement to other multimedia applications. A major limitation of individual object detectors is the significant number of misclassifications that occur because of the similarities in color and texture characteristics of various object types and lack of context information. We have developed a spatial context-aware object-detection system that first combines the output of individual object detectors to produce a composite belief vector for the objects potentially present in an image. Spatial context constraints, in the form of probability density functions obtained by learning, are subsequently used to reduce misclassification by constraining the beliefs to conform to the spatial context models. Experimental results show that the spatial context models improve the accuracy of natural object detection by 13% over the individual object detectors themselves. |
| Sponsorship | IEEE Signal Process. Soc., Comput. Soc., Circuits & Syst. Soc, Commun. Soc |
| File Size | 350136 |
| File Format | |
| ISBN | 0780379659 |
| DOI | 10.1109/ICME.2003.1221652 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-07-06 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Object detection Layout Context modeling Detectors Snow Probability density function Image retrieval Content based retrieval Humans Image segmentation |
| Content Type | Text |
| Resource Type | Article |
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