The electroencephalogram (EEG) signals are commonly used for diagnosis of epilepsy. In this paper, we present a new methodology for EEG based automated diagnosis of epilepsy. Our method involves detection of key-points at multiple scales in EEG signals using a pyramid of difference of Gaussian (DoG) filtered signals. Local binary patterns (LBP) are computed at these key-points and the histogram of these patterns are considered as the feature set, which is fed to the support vector machine (SVM) for classification of EEG signals. The proposed methodology has been investigated for the four wellknown classification problems namely, (i) normal and epileptic seizure, (ii) epileptic seizure and seizure-free, and (iii) normal, epileptic seizure, and seizure-free, (iv) epileptic seizure and nonseizure EEG signals using publically available university of Bonn EEG database. Our experimental results in terms of classification accuracies have been compared with existing methods for classification of the above mentioned problems. Further, performance evaluation on another EEG dataset shows that our approach is effective for classification of seizure and seizure-free EEG signals. The proposed methodology based on LBP computed at key-points is simple and easy to implement for realtime epileptic seizure detection.