Substance Based Image Classification using Wavelet Neural Network

Background: Substance based Image Classification (SIC) using a wavelet neural network can use for efficient recognition. The foremost task of SIC is to remove the surrounding regions from an image to reduce the misclassified portion and to effectively reflect a shape of an object. At first, the image to be extracted is performed with SIC system through the segmentation of the image. Next, in order to attain more accurate information, with the extracted set of regions the wavelet transform is applied for extracting the configured set of features. Finally, using the neural network classifier model, misclassification over the given natural images and further background images are removed from the given natural image using the LSEG segmentation. Moreover, to increase the accuracy of object classification, SIC system involves the removal of the regions in the surrounding image.Objective: The main objective is provide better object recognition system for object classification with more accuracy with less error rate using substance based image classification. Results: Experimental results with natural image dataset substance based image classification are complementary to existing region boundary representation model. Performance evaluation reveals that the proposed SIC system reduces the occurrence of misclassification and reflects the exact shape of an object to approximately 10-15%. Conclusion: To reduce the misclassification over the given image data, the background regions are removed from the given image data based by adapting the LSEG segmentation. To obtain the more accurate information from the image object data, the wavelet transform is applied to obtain the configured quality features. Based on the feature set, the information about the image objects data from the region boundary images are obtained. Besides, the object classifier is implemented for classification of image to obtain the exact shape of the object. Experimental evaluation is conducted with the natural image dataset to check the performance of the proposed SIC system. Evaluation results revealed that the proposed SIC system achieved a higher classification rate by removing the surrounding regions of the image. Moreover, the feature extraction process provides the highest classification rate which enhances the performance of substance based image classification system. At the same time, the proposed SIC system revealed that the occurrence of misclassification of image data is less and the acquiring the image object data in the rate of 13% compared to the existing work

Object Classification Using Substance Based Neural Network

Object recognition has shown tremendous increase in the field of image analysis. The required set of image objects is identified and retrieved on the basis of object recognition. In this paper, we propose a novel classification technique called substance based image classification (SIC) using a wavelet neural network. The foremost task of SIC is to remove the surrounding regions from an image to reduce the misclassified portion and to effectively reflect the shape of an object. At first, the image to be extracted is performed with SIC system through the segmentation of the image. Next, in order to attain more accurate information, with the extracted set of regions, the wavelet transform is applied for extracting the configured set of features. Finally, using the neural network classifier model, misclassification over the given natural images and further background images are removed from the given natural image using the LSEG segmentation. Moreover, to increase the accuracy of object classification, SIC system involves the removal of the regions in the surrounding image. Performance evaluation reveals that the proposed SIC system reduces the occurrence of misclassification and reflects the exact shape of an object to approximately 10–15%

Geometric and Topological Inference

Cambridge Texts in Applied MathematicsInternational audienceGeometric and topological inference deals with the retrieval of information about a geometric object that is only known through a finite set of possibly noisy sample points. Geometric and topological inference employs many tools from Computational Geometry and Applied Topology. It has connections to Manifold Learning and provides the mathematical and algorithmic foundations of the rapidly evolving field of Topological Data Analysis. Building on a rigorous treatment of simplicial complexes and distance functions, this book covers various aspects of the field, from data representation and combinatorial questions to manifold reconstruction and persistent homology. This first book on the subject can serve for teaching in a mathematical or computer science department, and will benefit to scientists and engineers interested in a geometric approach to Data Science