Open issues on codebook generation in Image Classification tasks

In the last years the use of the so-called bag-of-features approach, often referred to also as the codebook approach, has extensively gained large popularity among researchers in the image classification field, as it exhibited high levels of performance. A large variety of image classification, scene recognition, and more in general computer vision problems have been addressed according to this paradigm in the recent literature. Despite the fact that some papers questioned the real effectiveness of the paradigm, most of the works in the literature follows the same approach for codebook creation, making it a standard \textit{``de facto''}, without any critical investigation on the suitability of the employed procedure to the problem at hand. The most widespread structure for codebook creation is made up of four steps: dense sampling image patch detection; use of SIFT as patch descriptors; use of the $k$-means algorithms for clustering patch descriptors in order to select a small number of representative descriptors; use of the SVM classifier, where images are described by a codebook whose vocabulary is made up of the selected representative descriptors. In this paper, we will focus on a critical review of the third step of this process, to see if the clustering step is really useful to produce effective codebooks for image classification tasks. Reported results clearly show that a codebook created according to a purely random extraction of the patch descriptors from the set of descriptors extracted from the images in a dataset, is able to improve classification performances with respect to the performances attained with codebooks created by the clustering process.