Automatic image orientation detection
| Autor: | Aditya Vailaya, Feng-I Liu, Anil K. Jain, Hong-Jiang Zhang, Changjiang Yang |
|---|---|
| Rok vydání: | 2008 |
| Předmět: |
Learning vector quantization
Contextual image classification business.industry Feature vector Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Codebook Pattern recognition Linear discriminant analysis Computer Graphics and Computer-Aided Design Support vector machine ComputingMethodologies_PATTERNRECOGNITION Test set Artificial intelligence business Software Mathematics |
| Zdroj: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 11(7) |
| ISSN: | 1057-7149 |
| Popis: | We present an algorithm for automatic image orientation estimation using a Bayesian learning framework. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a learning vector quantizer (LVQ) can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. We further show how principal component analysis (PCA) and linear discriminant analysis (LDA) can be used as a feature extraction mechanism to remove redundancies in the high-dimensional feature vectors used for classification. The proposed method is compared with four different commonly used classifiers, namely k-nearest neighbor, support vector machine (SVM), a mixture of Gaussians, and hierarchical discriminating regression (HDR) tree. Experiments on a database of 16 344 images have shown that our proposed algorithm achieves an accuracy of approximately 98% on the training set and over 97% on an independent test set. A slight improvement in classification accuracy is achieved by employing classifier combination techniques. |
| Databáze: | OpenAIRE |
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