This research examines how comparison of objects underlies free categorization, an essential component of human cognition. Previous results using our binomial labeling task have shown that classification probabilities are affected in a graded manner as a function of similarity, i.e., the number of features shared by two objects. In a similarity rating task, people also rated objects sharing more features as more similar. However, the effect of matching features was approximately linear in the similarity task, but superadditive (exponential) in the labeling task. We hypothesize that this difference is due to the fact that people must select specific objects to compare prior to deciding whether to put them in the same category in the labeling task, while they were given specific pairs to compare in the rating task. Thus, the number of features shared by two objects could affect both stages (selection and comparison) in the labeling task, which might explain their super-additive effect, whereas it affected only the latter comparison stage in the similarity rating task. In this experiment, participants saw visual displays consisting of 16 objects from three novel superordinate artificial categories, and were asked to generate binomial (letter-number) labels for each object to indicate their super-and-subordinate category membership. Only one object could be viewed at a time, and these objects could be viewed in any order. This made it possible to record what objects people examine when labeling a given object, which in turn permits separate assessment of stage 1 (selection) versus stage 2 (comparison/decision). Our primary objective in this experiment was to determine whether the increase in category labeling probabilities as a function of level of match (similarity) can be explained by increased sampling alone (stage 1 model), an increased perception of similarity following sampling (stage 2 model), or some combination (mixed model). The results were consistent with earlier studies in showing that the number of matching discrete features shred by two objects affected the probability of same-category label assignment. However, there was no effect of the level of match on the probability of visiting the first matching object while labeling the second. This suggests that the labeling effect is not due to differences in the likelihood of comparing matching objects (stage 1) as a function of the level of match. Thus, the present data provides support for a stage 2 only model, in which the evaluation of similarity is the primary component underlying the level of match effect on free categorization.

Approximate Nearest-Neighbour Field (ANNF\ maps between two related images are commonly used by computer vision and graphics community for image editing, completion, retargetting and denoising. In this work we generalize ANNF computation to unrelated image pairs. For accurate ANNF map computation we propose Feature Match, in which the low-dimensional features approximate image patches along with global colour adaptation. Unlike existing approaches, the proposed algorithm does not assume any relation between image pairs and thus generalises ANNF maps to any unrelated image pairs. This generalization enables ANNF approach to handle a wider range of vision applications more efficiently. The following is a brief description of the applications developed using the proposed Feature Match framework. The first application addresses the problem of detecting the optic disk from retinal images. The combination of ANNF maps and salient properties of optic disks leads to an efficient optic disk detector that does not require tedious training or parameter tuning. The proposed approach is evaluated on many publicly available datasets and an average detection accuracy of 99% is achieved with computation time of 0.2s per image. The second application aims to super-resolve a given synthetic image using a single source image as dictionary, avoiding the expensive training involved in conventional approaches. In the third application, we make use of ANNF maps to accurately propagate labels across video for segmenting video objects. The proposed approach outperforms the state-of-the-art on the widely used benchmark SegTrack dataset. In the fourth application, ANNF maps obtained between two consecutive frames of video are enhanced for estimating sub-pixel accurate optical flow, a critical step in many vision applications. Finally a summary of the framework for various possible applications like image encryption, scene segmentation etc. is provided.