Abstract
We propose a generic model to classify a given image by detecting a specific image patch. Employing MPEG-7 features along with feature selection populates the feature space which is used to train using SVM classier. Our work target toward classifying objects in an unclassified image. We propose a model that can detect objects through generic framework for larger classes. Our model gives an overall accuracy of over 97%.
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Data set: MCIndoor20000 .https://github.com/bircatmcri/MCIndoor20000
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Chaudhuri, D.R., Chandra, D., Mittal, A. (2020). Indoor Object Classification Using Higher Dimensional MPEG Features. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore. https://doi.org/10.1007/978-981-15-0035-0_47
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DOI: https://doi.org/10.1007/978-981-15-0035-0_47
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