IDENTIFYING AND LEARNING VISUAL ATTRIBUTES FOR OBJECT RECOGNITION

We propose an attribute centric approach for visual object recognition. The attributes of an object are the observable visual properties that help to uniquely describe it. We present methods for identifying and learning these object attributes. To identify suitable object attributes, we process the corresponding Wikipedia pages to select terms that not only have high occurrence frequency, the images of these concepts must also be visually consistent. To learn object attributes, we assume prior knowledge of the object class-specific distributions of patches over the attributes, and introduce a novel algorithm that iteratively refines these distributions by a nearest-neighbor attribute classifier. Given an unseen image, its attribute vector is first formed by the distribution of patches over the attributes, and its final class is then determined by the attribute representation. We report efficacy of the proposed framework on an animal data set of ten classes, where the test set consists of images collected from the web.