Abstract
This paper presents an efficient method for object detection in natural scenes. It is accomplished via generalized Hough transform of distinctive midlevel parts. These parts are more meaningful than low-level patches such as lines or corners and would be able to cover the key structures of object. We collect the initial sets of parts by clustering with k-means in WHO space and train LDA model for every cluster. The codebooks are generated by applying the trained detectors to discover parts in whole positive training images and storing their spatial distribution relative to object center. When detecting in a new image, the energy map is formed by the voting from every entry in codebook and is used to predict the location of object. Experiment result shows the effectiveness of the proposed scheme.
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Kuang, X., Sang, N., Chen, F., Wang, R., Gao, C. (2014). Hough Voting with Distinctive Mid-Level Parts for Object Detection. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_31
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DOI: https://doi.org/10.1007/978-3-662-45646-0_31
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