Static video summarization techniques aim to represent the salient content in a video by extracting a set of key-frames for presentation to the user. An efficient key-frame extraction process is thus vital for effective video summarization, browsing and indexing in content-based video retrieval systems. In this paper, a three phased approach for key-frame extraction is proposed which aims to represent a static summary of a video. The first phase deals with detecting the best representative frame(s) for each shot in the video. The second and third phases comprise of techniques for intra-shot and inter-shot redundancy reduction using SURF and GIST on the extracted key-frames in the first phase. At the end of each phase, comparison between the system generated summary and user summary is performed. A comparative analysis between SURF and GIST for redundancy reduction is also presented. Experimental evaluation of the results on a test set of videos from sports, movie songs, music albums and documentaries show that the proposed method achieves high precision and recall values in all the cases.