Many cloud providers such as Microsoft, Amazon, and Google offer scalable computing environment with pay-per-use. However, processing large-scale data using on-demand cloud instances may still be too costly. Archival data, unlike real-time streams, does not have strict time constraints. Thus, it does not require continuous processing and occasional suspension can be tolerated. Some cloud vendors (such as Amazon) introduces spot instances that use spare instances with dynamic pricing. Spot instances offer the same performance as on-demand instances at greatly reduced prices but spot instances may be terminated at short notice. As a result, processing programs may not finish when using spot instances. This paper introduces a cost-effective system to process large-scale image data using Amazon EC2 (Elastic Compute Cloud) spot instances and Amazon Simple Storage Service (S3). This system uses a check-pointing method to store progress so that processing can resume later if the spot instances are terminated. Even though using spot instances may prolong the total execution time, our experiments demonstrate that with appropriate bidding strategies, the execution time can be almost the same as using on-demand instances, while saving up to 85% cost.