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; · 3.11 Impact Factor
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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
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ABSTRACT: The recognition of objects from imagery in a manner that is independent of scale, position and orientation may be achieved by characterizing an object with a set of extracted invariant features. Several different recognition techniques have been demonstrated that utilize moments to generate such invariant features. These techniques are derived from general moment theory which is widely used throughout statistics and mechanics. In this paper, basic Cartesian moment theory is reviewed and its application to object recognition and image analysis is presented. The geometric properties of low-order moments are discussed along with the definition of several moment-space linear geometric transforms. Finally, significant research in moment-based object recognition is reviewed.CVGIP Graphical Models and Image Processing 09/1992;