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14 December 2015 An enhanced MIML algorithm for natural scene image classification
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Proceedings Volume 9813, MIPPR 2015: Pattern Recognition and Computer Vision; 98130O (2015) https://doi.org/10.1117/12.2204723
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China
Abstract
The multi-instance multi-label (MIML) learning is a learning framework where each example is described by a bag of instances and corresponding to a set of labels. In some studies, the algorithms are applied to natural scene image classification and have achieved satisfied performance. We design a MIML algorithm based on RBF neural network for the natural scene image classification. In the framework, we compare classification accuracy based on the existing definitions of bag distance: maximum Hausdorff, minimum Hausdorff and average Hausdorff. Although the accuracy of average Hausdorff bag distance is the highest, we find average Hausdorff bag distance to weaken the role of the minimum distance between the instances in the two bags. So we redefine the average Hausdorff bag distance by introducing an adaptive adjustment coefficient, and it can change according to the minimum distance between the instances in the two bags. Finally, the experimental results show that the enhanced algorithm has a better result than the original algorithm.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Wei Wu, Hui Zhang, and Suyan Yang "An enhanced MIML algorithm for natural scene image classification", Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130O (14 December 2015); https://doi.org/10.1117/12.2204723
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