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Feature extraction (FE) is an important step in image retrieval, image processing, data mining and computer vision. FE is the process of extracting relevant information from raw data. However, the problem of extracting appropriate features that can reflect the intrinsic content of a piece of data or dataset as complete as possible is still a challenge for most FE techniques. In this paper, we present a survey of the existing FE techniques used in recent times. In this study, it was observed that the most unique features that can be extracted when using GLDS features on images are contrast, homogeneity, entropy, mean and energy. In addition, it was observed that FE techniques are not mainly application specific but can be applied to several applications.