Finding similar images is a necessary operation in many multimedia applications. Images are often represented and stored as a set of high-dimensional features, which are extracted using localized feature extraction algorithms. Locality Sensitive Hashing is one of the most popular approximate processing techniques for finding similar points in high-dimensional spaces. Locality Sensitive Hashing (LSH) and its variants are designed to find similar points, but they are not designed to find objects (such as images, which are made up of a collection of points) efficiently. In this paper, we propose an index structure, Bitmap-Image LSH (bImageLSH), for efficient processing of high-dimensional images. Using a real dataset, we experimentally show the performance benefit of our novel design while keeping the accuracy of the image results high.