In the world, Diabetic Retinopathy is the leading cause of vision loss. Early symptoms of this disease are exudates, so early diagnosis and treatment at right time is very important to prevent blindness. For a particularly long time, automatic diagnosis of diabetic retinopathy from digital fundus images has been an active research topic in the medical image processing community. In this work, two new methods for the detection of exudates are presented which do not use a supervised learning step; therefore, they do not require labelled lesion training sets which are time consuming to create, difficult to obtain and prone to human error. We introduce a new dataset of fundus images from various ethnic groups and levels of DME which we have made publicly available. Experimental results show that proposed yields better results over state of art methods.