Semantic Hough Transform based object detection with Partial Least Squares

The codebooks play a decisive role in the Hough Transform based object detection. We propose a novel approach to generate the codebooks in the manner of parametric regression and integrate inside semantic information drawn from objects and background. Clustering is a popular method for deriving codebooks, but it generally relies on some parameters, which heavily affect the performance of the approaches. By exploiting Partial Least Squares and tuning only one parameter, we map the most informative latent components of an image patch directly to the displacement vectors from the possible object centroids to the patch, and obtain the Parameterized Semantic Codebook Group (PSCG). Experiments show that PSCG generates accurate voting vectors and performs superiorly on some challenging datasets.