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In this paper, we propose a new scheme aimed for gastrointestinal (GI) tumor capsule endoscopy (CE) images classification, which utilizes sequential forward floating selection (SFFS) together with support vector machine (SVM). To achieve this goal, candidate features related to texture characteristics of CE images are extracted. With these candidate features, SFFS based on SVM is applied to select the most discriminative features that can separate normal CE images from tumor CE images. Comprehensive experiments on our present CE image data verify that it is promising to employ the proposed scheme to recognize tumor CE images.