A Non-Parametric Trainabie Object-Detection Model Using a Concept of Retinotopic Sampling

摘要

A retina has a space-variant sampling mechanism and an orientation-sensitive mechanism. The space-variant sampling mechanism of the retina is called retinotopic sampling (RS). With these mechanisms of the retina, the object-detection is formulated as finding appropriate coordinate transformation from a coordinate system on an input image, to a coordinate system on the retina. However, when the object size is inferred by this mechanism, the result tends to gravitate towards zero. To cancel this gravity, the space-variant sampling mechanism, is modified to uniform sampling mechanism, but a concept of RS is equivalently introduced by using space-variant weights. This object-detection mechanism is modeled as a non-parametric method. By using the model based on RS, we formulate a kernel function as an analytical function of information of an object, a position and a size of the object in an image. Then the object-detection is realized as a gradient decent method for a discriminant function trained by Support Vector Machine (SVM) using this kernel function. This detection mechanism realizes faster detection than exploring a visual scene in raster-like fashion. The discriminant function outperforms results of SVMs using a kernel function using intensities of all pixels (based on independently published results), in face detection experiments over the 24,045 test images in the MIT-CBCL database.