Eigenregions for image classification

For certain databases and classification tasks, analyzing images based on region features instead of image features results in more accurate classifications. We introduce eigenregions, which are geometrical features that encompass area, location, and shape properties of an image region, even if the region is spatially incoherent. Eigenregions are calculated using principal component analysis (PCA). On a database of 77,000 different regions obtained through the segmentation of 13,500 real-scene photographic images taken by nonprofessional, eigenregions improved the detection of localized image classes by a noticeable amount. Additionally, eigenregions allow us to prove that the largest variance in natural image region geometry is due to its area and not to shape or position.     

A flow-based approach to modeling ADSL traffic on an IP backbone link

Measurements from an Internet backbone link carryingtcp traffic towards differentadsl areas are analyzed in this paper. For traffic analysis, we adopt a flow-based approach and the popular mice/elephants dichotomy. The originality of the experimental data reported in this paper, when compared with previous measurements from very high speed backbone links, is that commercial traffic comprises a significant part due to peer-to-peer applications. This kind of traffic exhibits some remarkable properties in terms of mice, elephants and bit rates, which are thoroughly described in this paper. Global traffic is actually decomposed into several flow components on the basis of the mice/elephants dichotomy. Mice due p2p protocols and mice due to classical Internet applications such ashttp, ftp, etc. are analyzed separately. It turns out that by adopting a suitable level of aggregation, global traffic can be described by means of usual tele-traffic models based on M/G/∞ queues with Weibullian service times. The blobal bit rate can then be approximated by the superposition of Gaussian processes perturbed by a white noise.