Abstract
Researchers in the field of image understanding face the challenging issue of conceptual description at semantic level based on primitive visual features. The work presented in this paper attempts to provide semantic description of an image. The first objective of the proposed research is to extract features based on human perception from natural scene images. A mapping process is used to obtain high-level semantic features based on color, texture and structural features to tackle the problem of semantic gap. The second focus is to construct a classification model using classification rules mined from training images. The mined rules are easily understandable and have the advantage of supporting user’s view of categorizing an image in a particular class. Two state of the art data mining algorithms: Classification based association (CBA) and decision tree induction are empirically evaluated for the purpose of classification of natural scenes. The results obtained by 10-fold cross-validation approach show that classification rules extracted using ID3 algorithm yield good performance with an average accuracy rate of 83.8%.
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Shrivastava, P., Bhoyar, K.K., Zadgaonkar, A.S. (2018). Mining Efficient Rules for Scene Classification Using Human-Inspired Features. In: Saeed, K., Chaki, N., Pati, B., Bakshi, S., Mohapatra, D. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 564. Springer, Singapore. https://doi.org/10.1007/978-981-10-6875-1_16
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DOI: https://doi.org/10.1007/978-981-10-6875-1_16
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