Abstract.
Bag-of-features representations have recently become popu- lar for content based image classification owing to their simplicity and good performance. They evolved from texton methods in texture analy- sis. The basic idea is to treat images as loose collections of independent patches, sampling a representative set of patches from the image, evalu- ating a visual descriptor vector for each patch independently, and using the resulting distribution of samples in descriptor space as a characteri- zation of the image. The four main implementation choices are thus how to sample patches, how to describe them, how to characterize the re- sulting distributions and how to classify images based on the result. We concentrate on the first issue, showing experimentally that for a repre- sentative selection of commonly used test databases and for moderate to large numbers of samples, random sampling gives equal or better classifiers than the sophisticated multiscale interest operators that are in common use. Although interest operators work well for small numbers of samples, the single most important factor governing performance is the number of patches sampled from the test image and ultimately in- terest operators can not provide enough patches to compete. We also study the influence of other factors including codebook size and creation method, histogram normalization method and minimum scale for feature extraction.