Abstract
Spatial pyramid matching (SPM) has been one of important approaches to image categorization. Despite its effectiveness and efficiency, SPM measures the similarity between sub-regions by applying the bag-of-features model, which is limited in its capacity to achieve optimal matching between sets of unordered features. To overcome this limitation, we propose a hierarchical spatial matching kernel (HSMK) that uses a coarse-to-fine model for the sub-regions to obtain better optimal matching approximations. Our proposed kernel can robustly deal with unordered feature sets as well as a variety of cardinalities. In experiments, the results of HSMK outperformed those of SPM and led to state-of-the-art performance on several well-known databases of benchmarks in image categorization, even when using only a single type of feature.
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Le, T.T., Kang, Y., Sugimoto, A., Tran, S.T., Nguyen, T.D. (2011). Hierarchical Spatial Matching Kernel for Image Categorization. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21593-3_15
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DOI: https://doi.org/10.1007/978-3-642-21593-3_15
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