Semantic image classification by genetic algorithm using optimised fuzzy system based on Zernike moments
ABSTRACT Image classification is a challenging problem of computer vision. This study reports a fuzzy system to semantic image classification. As it is a complex task, various information of digital image, including three color space components and two Zernike moments with different order, are gathered and utilized as an input of fuzzy inference system to materialize a robust rotation/lighting condition and size invariant image classifier. For better performance, all the membership functions are optimized by genetic algorithm after empirical design stage. 90.62 and 96.25 % classification rates for RGB and HSI color spaces confirm the reliability of optimized system in different image conditions given in this contribution.
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ABSTRACT: Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Using binary Bayesian classifiers, we attempt to capture high-level concepts from low-level image features under the constraint that the test image does belong to one of the classes. Specifically, we consider the hierarchical classification of vacation images; at the highest level, images are classified as indoor or outdoor; outdoor images are further classified as city or landscape; finally, a subset of landscape images is classified into sunset, forest, and mountain classes. We demonstrate that a small vector quantizer (whose optimal size is selected using a modified MDL criterion) can be used to model the class-conditional densities of the features, required by the Bayesian methodology. The classifiers have been designed and evaluated on a database of 6931 vacation photographs. Our system achieved a classification accuracy of 90.5% for indoor/outdoor, 95.3% for city/landscape, 96.6% for sunset/forest and mountain, and 96% for forest/mountain classification problems. We further develop a learning method to incrementally train the classifiers as additional data become available. We also show preliminary results for feature reduction using clustering techniques. Our goal is to combine multiple two-class classifiers into a single hierarchical classifierIEEE Transactions on Image Processing 02/2001; · 3.20 Impact Factor
Conference Paper: On image analysis by the methods of moments[Show abstract] [Hide abstract]
ABSTRACT: Various types of moments have been used to recognize image patterns in a number of applications. The authors evaluate a number of moments and addresses some fundamental questions, such as image representation ability, noise sensitivity, and information redundancy. Moments considered include regular moments, Legendre moments, Zernike moments, pseudo-Zernike moments, rotational moments and complex moments. Properties of these moments are examined in detail, and the interrelationships among them are discussed. Both theoretical and experimental results are presentedComputer Vision and Pattern Recognition, 1988. Proceedings CVPR '88., Computer Society Conference on; 07/1988
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ABSTRACT: While people compare images using semantic concepts, computers compare images using low-level visual features that sometimes have little to do with these semantics. To reduce the gap between the high-level semantics of visual objects and the low-level features extracted from them, in this paper we develop a framework of learning similarity (LS) using neural networks for semantic image classification, where a LS-based k-nearest neighbors (k-NNL) classifier is employed to assign a label to an unknown image according to the majority of k most similar features. Experimental results on an image database show that the k-NNL classifier outperforms the Euclidean distance-based k-NN (k-NNE) classifier and back-propagation network classifiers (BPNC).Neurocomputing 08/2005; · 2.01 Impact Factor