In this work we present a new approach for detecting loop closures in a real-time online setting. The Loop Closure Detection problem is important in visual SLAM applications and different approaches exist to deal with this problem. Most of these approaches are based on the Bag-of-Words approach, and assume a fixed visual vocabulary can work in different types of environments. However BOW is known to introduce perceptual aliasing. By using Locality Sensitive Hashing (LSH) we are able to compute image similarity and detect loop closures by using visual features directly without vector quantization as in BOW and also LSH does not require a prior visual vocabulary. We show the effectiveness of our approach empirically by comparing it to the Bag of Words (BOW) approach which is the dominant method of selecting candidate loop closing images. Our method is fast enough for realtime applications and its accuracy is significantly better than the BOW approach.