The success of the bag-of-words approach for text has inspired the recent use of analogous strategies for global representation of images with local visual features.
Many applications have been proposed for object detection, image annotation, queries-by-example, relevance feedback, automatic annotation, and clustering.
In this paper, we investigate the validity of the bag-of-words analogy for image representation and, more specifically,
local pattern selection for feature generation.
We propose a generalized document representation framework and apply it to the evaluation of two pattern selection strategies for images: dense sampling and point-of-interest detection.
We present empirical results that support our contention that text-based experimentation can provide useful insights into the effectiveness of image representations based on the bag-of-visual-words technique.