Perceptual Image Hashing Using Random Forest for Content-based Image Retrieval

Use of large image datasets has become a common occurrence. This, however, makes image searching a highly desired operation in many applications. Most of the content-based image retrieval (CBIR) methods usually adopt machine-learning techniques that take the image content into account. These methods are effective, but they are generally too complex and resource demanding. We propose a framework based on image hashing and random forest, which is fast and offers high performance. The proposed framework consists of a multi-key image hashing technique based on discrete cosine transform (DCT) and discrete wavelet transform (DWT) and random forest based on normalized B+ Tree (NB+ Tree), which reduces the high-dimensional input vectors to one-dimension, which in turn improves the time complexity significantly. We analyze our method empirically and show that it outperforms competitive methods in terms of both accuracy and speed. In addition, the proposed scheme maintains a fast scaling with increasing size of the data sets while preserving high accuracy.