Abstract
In this work we perform an extensive comparative study of approaches for mobile visual recognition by simultaneously evaluating the performance and the computational cost of state-of-the-art key-point detection, feature extraction and encoding algorithms. Every step is independently tested so that its contribution to the final computational cost can be measured. The widely used OpenCV library is utilized for the implementation of the algorithms, while the evaluation is performed on the PASCAL VOC 2007 dataset, a challenging real world dataset crawled from the web. Our study identifies the algorithmic configurations that manage to optimally balance performance and computational cost, and provide a viable solution for real time mobile visual recognition.
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Chatzilari, E., Liaros, G., Nikolopoulos, S., Kompatsiaris, Y. (2013). A Comparative Study on Mobile Visual Recognition. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2013. Lecture Notes in Computer Science(), vol 7988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39712-7_34
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DOI: https://doi.org/10.1007/978-3-642-39712-7_34
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