1. A method for detecting an object part location in images captured by at least one camera, said method comprising: defining with a relevance computing unit, a specific relevance value between a configuration of a plurality of parts and a set of training images annotated via an annotating object; applying a similarity learning algorithm with respect to said plurality of parts to obtain a learned similarity function based on said specific relevance value, wherein said learned similarity function comprises a real-valued function that quantifies a similarity between at least two objects and wherein said similarity learning algorithm respects a similarity between part configurations, wherein a similarity learning of said similarity learning algorithm comprises a supervised machine learning task, and wherein said learned similarity function assigns a high score to positive pairs and a low score to negative pairs; measuring a similarity between a new image captured by said at least one camera and said set of training images utilizing said learned similarity function in order to obtain a neighbor image and predict an object part location of a visible object part and a non-visible object part with respect to said new image based on said neighbor image; capturing with said at least one camera, said set of training images and said new image with respect to said annotating object; processing said set of training images and said new image utilizing an image processor and wherein said predict said object part location of said visible object part and said non-visible object part with respect to said new irnace based on said neighbor image, further comprises a detection of an object part, wherein said detection is executable as a single query that is independent of a number of object parts by taking into account a global consistency between said object parts; defining a set of indices corresponding to said visible object part in said training images among said set of training images and thereafter computing said specific relevance value between said training images among said set of training images; computing a part-by-part overlap score by measuring an overlap between similar parts; and assigning said specific relevance value to one if said part-by-part overlap score is greater than a threshold otherwise assign zero.