Abstract
We present the evaluation of a product identification task using the LIRe system and SURF (Speeded-Up Robust Features) for content-based image retrieval (CBIR). The evaluation is performed on the Fribourg Product Image Database (FPID) that contains more than 3’000 pictures of consumer products taken using mobile phone cameras in realistic conditions. Using the evaluation protocol proposed with FPID, we explore the performance of different preprocessing and feature extraction. We observe that by using SURF, we can improve significantly the performance on this task. Image resizing and Lucene indexing are used in order to speed up CBIR task with SURF. We also show the benefit of using simple preprocessing of the images such as a proportional cropping of the images. The experiments demonstrate the effectiveness of the proposed method for the product identification task.
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Chen, K., Hennebert, J. (2014). Content-Based Image Retrieval with LIRe and SURF on a Smartphone-Based Product Image Database. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-Lopez, J.A., Salas-Rodríguez, J., Suen, C.Y. (eds) Pattern Recognition. MCPR 2014. Lecture Notes in Computer Science, vol 8495. Springer, Cham. https://doi.org/10.1007/978-3-319-07491-7_24
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DOI: https://doi.org/10.1007/978-3-319-07491-7_24
Publisher Name: Springer, Cham
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