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Classification of large amount of images calls for diverse types of features, but employing all possible feature types will create unnecessary computation burden, and may result in reduced classification accuracy. Selecting feature vectors individually is not a feasible solution in this scenario due to the high amount of feature vectors needed for reasonable performance. Instead, this paper proposes...
Extreme learning machine (ELM) is one of suitable base-classifiers for ensemble learning systems because of its fast learning speed, good generalization performance and simple setting. For the ensemble learning, how to select the base classifiers is a key issue which influences the performance of the ensemble system dramatically. To obtain a compact ensemble system with improved generalization performance,...
Images of ancient maps and floor plans can present a great challenge for common character recognition tools. Besides the damage caused by time and handling, these documents have an important part of their information described graphically. In most examples, drawings of rivers or walls occupy most part of the document. Usually, text has different styles, sizes and orientations with possible overlapping...
Fuzzy c-means clustering algorithm (FCM) is often used for image segmentation but it is sensitive to noise. This paper presents an extended fuzzy local information c-means clustering algorithm for robust image segmentation. In this method, a novel fuzzy factor created by the neighborhood spatial and gray information is integrated into the objective function of FCM. The fuzzy factor can enhance the...
Spiking cortical model (SCM) is applied to image segmentation. A natural image is processed to produce a series of spike images by SCM, and the segmented result is obtained by the integration of the series of spike images. An appropriate maximum iterative times is selected to achieve an optimal threshold of SCM. In each iteration, neurons that produced spikes correspond to pixels with an intensity...
In this paper we describe a hybrid evolutionary-cellular automata based algorithm for the segmentation of multidimensional images, in particular hyperspectral images. This algorithm permits automatically generating the cellular automata transition rule set using as training set a group of appropriately generated synthetic RGB images, which greatly simplifies the process given the lack of adequately...
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