This paper presents a new and fast binary descriptor for image matching learned from Haar features. The training uses AdaBoost; the weak learner is built on response function for Haar features, instead of histogram-type features. The weak classifier is selected from a large weak feature pool. The selected features have different feature type, scale and position within the patch, having correspond threshold value for weak classifiers. Besides, to cope with the fact in real matching that dissimilar matches are encountered much more often than similar matches, cascaded classifiers are trained to motivate training algorithms see a large number of dissimilar patch pairs. The final trained output are binary value vectors, namely descriptors, with corresponding weight and perceptron threshold for a strong classifier in every stage. We present preliminary results which serve as a proof-of-concept of the work.