Abstract
The image data stream classification presents a number challenges, such as the evolution of previously known classes and the emergence of new classes. A large part of current studies for image data stream classification uses classifiers that are able to evolve. However, these do not take into consideration that the image descriptors also need to evolve, in order that these improve the representation of features on new images available in the stream. Nonetheless, studies that explore the evolution of image feature descriptors do not analyze a number of the scenarios that may occur in these applications. This work presents an experimental study on the construction of image feature descriptors for the image data stream classification, while considering different aspects, as in the fact that only part of the image instances can be made available or only part of the image classes are known at the moment of constructing the descriptors. Experiments were performed on 4 image datasets, considering the state-of-art descriptors, BoVW and CNN, as well as with an algorithm that considers the evolution of the image feature descriptor. The obtained results show that the performance of a classifier may degrade its performance when submitted to scenarios that were not explored in previous studies.
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
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de Lima, M.C., de Abreu, A.J.S., Faria, E.R., Barioni, M.C.N. (2021). Evaluating the Construction of Feature Descriptors in the Performance of the Image Data Stream Classification. In: Tavares, J.M.R.S., Papa, J.P., González Hidalgo, M. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2021. Lecture Notes in Computer Science(), vol 12702. Springer, Cham. https://doi.org/10.1007/978-3-030-93420-0_31
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