Abstract
Automatic analysis of image data is of high importance for many applications. Given an image classification problem one needs three things: (i) Training data and tools to extract (ii) relevant visual information—usually image features—that can be used by (iii) classification algorithms. For given (i), a multitude of candidates present themselves for (ii) and (iii). Model selection becomes the main issue. We present a web-based feature benchmark system enabling system designers to streamline tool-chains to specific needs using available implementations of candidate tools. Our system features a modular architecture, remote and parallel computing, extensibility and—from a user’s standpoint—platform independence due to its web-based nature. Using Wifbs, image features can be subjected to a sophisticated and unbiased model selection procedure to compose optimized pipelines for given image classification problems.
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Spehr, M., Grottel, S., Gumhold, S. (2015). Wifbs: A Web-Based Image Feature Benchmark System. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds) MultiMedia Modeling. MMM 2015. Lecture Notes in Computer Science, vol 8936. Springer, Cham. https://doi.org/10.1007/978-3-319-14442-9_14
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DOI: https://doi.org/10.1007/978-3-319-14442-9_14
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