Multiresolution Histograms and their Use for Texture Classification

The histogram of image intensities is used extensively for the retrieval of images from visual databases. An obvious way to extend this feature is to compute the histograms of multiple resolutions of an image. Both this extension and the plain histogram are fast to compute, space efficient, invariant to rigid motions, and robust to noise. In addition, the histograms over multiple image resolutions directly encode texture information. We describe a simple yet novel matching algorithm based on this extension. We evaluate it on two texture databases against algorithms based on five widely used texture features. We show that with our simple algorithm, we achieve or exceed the performance, robustness, and efficiency of more complicated features. 1 The Multiresolution Histogram The histogram of image intensities has proven to be a robust and efficient representation for indexing visual databases [15]. Histograms, however, don’t encode texture information. The multiresolution decomposition of an image does encode texture information. The histograms of Gaussian blurred versions of an image, as shown in Fig. 1, encode the interactions between intensities of neighboring parts of the image. We call this sequence of histograms the multiresolution histogram. This representation retains many important properties of the histogram. It is fast to compute, space efficient, and invariant to rigid motions. The inherent blurring also makes it robust to noise. All these properties make this an effective texture feature. In this work we make the following contributions: (1)We present a simple yet novel texture feature based on the multiresolution histogram. (2) We evaluate the performance of our feature. It gives excellent results on two texture databases while maintaining considerable robustness to illumination. (3)We compare our feature with five of the most commonly used texture features. We show that our simple feature is comparable or better in terms of discriminability, robustness, and efficiency.