Many object recognition systems train a different classifier for each object category and use the sliding window approach to classify image regions. In this talk, we pose the object recognition problem as data association where a novel object is explained solely in terms of a small set of exemplar objects to which it is visually similar. We learn a different distance function for each exemplar such that the returned distances can be interpreted to detect the presence of an object. Our exemplars are represented as image regions and the learned distances capture the relative importance of shape, color, texture, and position features for that region. We use the distance functions to detect and segment objects in novel images by associating the bottom-up segments obtained from multiple image segmentations with the exemplar regions. We evaluate the detection and segmentation performance of our algorithm on real-world outdoor scenes from the LabelMe dataset and also show some qualitative image parsing results.