Abstract
Probabilistic topic models have been applied to image classification and permit to obtain good results. However, these methods assumed that all topics have an equal contribution to classification. We propose a weight learning approach for identifying the discriminative power of each topic. The weights are employed to define the similarity distance for the subsequent classifier, e.g. KNN or SVM. Experiments show that the proposed method performs effectively for image classification.
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Liu, Y., Caselles, V. (2011). Image Classification Based on Weighted Topics. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24958-7_31
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DOI: https://doi.org/10.1007/978-3-642-24958-7_31
Publisher Name: Springer, Berlin, Heidelberg
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