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Indoor scene recognition is a challenging problem in the classical scene recognitiondomain due to the severe intra-class variations and inter-class similarities ofman-made indoor structures. State-of-the-art scene recognition techniques suchas capturing holistic representations of an image demonstrate low performance onindoor scenes. Other methods that introduce intermediate steps such as identifyingobjects and associating them with scenes have the handicap of successfullylocalizing and recognizing the objects in a highly cluttered and sophisticatedenvironment.We propose a classication method that can handle such diculties of theproblem domain by employing a metric function based on the nearest-neighborclassication procedure using the bag-of-visual words scheme, the so-called codebooks.Considering the codebook construction as a Voronoi tessellation of thefeature space, we have observed that, given an image, a learned weighted distanceof the extracted feature vectors to the center of the Voronoi cells gives a strongindication of the image's category. Our method outperforms state-of-the-art approacheson an indoor scene recognition benchmark and achieves competitiveresults on a general scene dataset, using a single type of descriptor.In this study although our primary focus is indoor scene categorization, we alsoemploy the proposed metric function to create a baseline implementation for theauto-annotation problem. With the growing amount of digital media, the problemof auto-annotating images with semantic labels has received signicant interestfrom researches in the last decade. Traditional approaches where such content ismanually tagged has been found to be too tedious and a time-consuming process.Hence, succesfully labeling images with keywords describing the semantics is acrucial task yet to be accomplished.