Three features for image classification into natural objects and artifacts are investigated. They are 'line length ratio', 'line direction distribution,' and 'edge coverage'. Among the three, the feature 'line length ratio' shows superior classification accuracy (above 90%) that exceeds the performance of conventional features, according to experimental results in application to digital camera images. As the development of this feature was motivated by the fact that the edge sharpening magnitude in image-quality improvement must be controlled based on the image content, this classification algorithm should be especially suitable for the image-quality improvement applications.

In this paper we propose an efficient line feature-based 2D object recognition algorithm using a novel entropy correspondence measure (ECM) that encodes the probabilistic similarity between two line feature sets. Since the proposed ECM-based method uses the whole structural information of objects simultaneously for matching, it overcomes the common drawbacks of the conventional techniques that are based on feature to feature correspondence. Moreover, since ECM is endowed with probabilistic attribute, it shows quite robust performance in the noisy environment. In order to enhance the recognition performance and speed, line features are pre-clustered into several groups according to their inclination by an eigen analysis, and then ECM is applied to each corresponding group individually. Experimental results on real images demonstrate that the proposed algorithm has superior performance to those of the conventional algorithms in both the accuracy and the computational efficiency, in the noisy environment.