Abstract
In this paper we compare two state-of-the-art approaches for image classification. The first approach follows the Bag-of-Keypoints method for classifying images based on local image pattern frequency distribution. The second approach computes the gist of an image by computing global image statistics. Both approaches are explained in detail and their performance is compared using a subset of images taken from the ImageClef 2011 PhotoAnnotation task. The images were selected based on the assumption they could be better described using global features. Results show that while Bag-of-Keypoints-like classification performs better even for global concepts the classification accuracy of the global descriptor remains acceptable at a much smaller computational footprint.
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Hentschel, C., Gerke, S., Mbanya, E. (2013). Classifying Images at Scene Level: Comparing Global and Local Descriptors. In: Detyniecki, M., GarcÃa-Serrano, A., Nürnberger, A., Stober, S. (eds) Adaptive Multimedia Retrieval. Large-Scale Multimedia Retrieval and Evaluation. AMR 2011. Lecture Notes in Computer Science, vol 7836. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37425-8_6
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DOI: https://doi.org/10.1007/978-3-642-37425-8_6
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