Abstract-This paper introduces three new contributions to the problems of image classification and image search. First, we propose a new image patch quantization algorithm. Other competitive approaches require a large code book and the sampling of many local regions for accurate image description, at the expense of a prohibitive processing time. We introduce Extremely Randomized Clustering Forests-ensembles of randomly created clustering trees-that are more accurate, much faster to train and test, and more robust to background clutter compared to state-of-the-art methods. Second, we propose an efficient image classification method that combines ERC-Forests and saliency maps very closely with image information sampling. For a given image, a classifier builds a saliency map online, which it uses for classification. We demonstrate speed and accuracy improvement in several state-of-the-art image classification tasks. Finally, we show that our ERC-Forests are used very successfully for learning distances between images of never-seen objects. Our algorithm learns the characteristic differences between local descriptors sampled from pairs of the "same" or "different" objects, quantizes these differences with ERC-Forests, and computes the similarity from this quantization. We show significant improvement over state-of-the-art competitive approaches.
Many machine learning processes have demonstrated good classification of image categories using image features REF and ensembles of randomly created clustering trees.
Randomized Clustering Forests for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Science Medicine