Abstract: Present image processing algorithms are unable to extract a neat and closed contour of an object of interest from a natural image. Advanced contour detection algorithms extract the contour of an object of interest from a natural scene with a side effect of depletion of the contour. Hence in order to perform well in a real world scenario, object recognition algorithms should be robust to contour incompleteness. With inspiration from psychophysical studies of the human cognitive abilities we propose a novel method to evaluate the performance of object recognition algorithms in terms of their robustness to incomplete contour representations. Complete contour representations of objects are used as a reference (training) set. Incomplete contour representations of the same objects are used as a test set. The performance of an algorithm is evaluated using the recognition rate as a function of the percentage of contour retained. The test framework is illustrated by using two contour based shape recognition algorithms which use a shape context and a distance multiset as shape descriptors. Three types of contour incompleteness, viz. segment-wise contour deletion, occlusion and random pixel depletion, are considered. In our experiments we use images from the COIL and MPEG-7 datasets. Both algorithms qualitatively perform similar to the human visual system in the sense that recognition performance monotonously increases with the degree of completeness and that they perform best in the case of random depletion and worst in the case of occluded contours. The distance multiset shape descriptor outperforms the shape context in this test especially for high levels of incompleteness.
