The Visual Extent of an Object

The visual extent of an object reaches beyond the object itself. This is a long standing fact in psychology and is reflected in image retrieval techniques which aggregate statistics from the whole image in order to identify the object within. However, it is unclear to what degree and how the visual extent of an object affects classification performance. In this paper we investigate the visual extent of an object on the Pascal VOC dataset using a Bag-of-Words implementation with (colour) SIFT descriptors. Our analysis is performed from two angles. (a) Not knowing the object location, we determine where in the image the support for object classification resides. We call this the normal situation. (b) Assuming that the object location is known, we evaluate the relative potential of the object and its surround, and of the object border and object interior. We call this the ideal situation. Our most important discoveries are: (i) Surroundings can adequately distinguish between groups of classes: furniture, animals, and land-vehicles. For distinguishing categories within one group the surroundings become a source of confusion. (ii) The physically rigid plane, bike, bus, car, and train classes are recognised by interior boundaries and shape, not by texture. The non-rigid animals dog, cat, cow, and sheep are recognised primarily by texture, i.e. fur, as their projected shape varies greatly. (iii) We confirm an early observation from human psychology (Biederman in Perceptual Organization, pp. 213-263, 1981): in the ideal situation with known object locations, recognition is no longer improved by considering surroundings. In contrast, in the normal situation with unknown object locations, the surroundings significantly contribute to the recognition of most classes

The role of sulfoglucuronosyl glycosphingolipids in the pathogenesis of monoclonal IgM paraproteinemia and peripheral neuropathy

In IgM paraproteinemia and peripheral neuropathy, IgM M-protein secretion by B cells leads to a T helper cell response, suggesting that it is antibody-mediated autoimmune disease involving carbohydrate epitopes in myelin sheaths. An immune response against sulfoglucuronosyl glycosphingolipids (SGGLs) is presumed to participate in demyelination or axonal degeneration in the peripheral nervous system (PNS). SGGLs contain a 3-sulfoglucuronic acid residue that interacts with anti-myelin-associated glycoprotein (MAG) and the monoclonal antibody anti-HNK-1. Immunization of animals with sulfoglucuronosyl paragloboside (SGPG) induced anti-SGPG antibodies and sensory neuropathy, which closely resembles the human disease. These animal models might help to understand the disease mechanism and lead to more specific therapeutic strategies. In an in vitro study, destruction or malfunction of the blood-nerve barrier (BNB) was found, resulting in the leakage of circulating antibodies into the PNS parenchyma, which may be considered as the initial key step for development of disease

Real-time bag of words, approximately

We start from the state-of-the-art Bag of Words pipeline that in the 2008 benchmarks of TRECvid and PASCAL yielded the best performance scores. We have contributed to that pipeline, which now forms the basis to compare various fast alternatives for all of its components: (i) For descriptor extraction we propose a fast algorithm to densely sample SIFT and SURF, and we compare several variants of these descriptors. (ii) For descriptor projection we compare a k-means visual vocabulary with a Random Forest. As a preprojection step we experiment with PCA on the descriptors to decrease projection time. (iii) For classification we use Support Vector Machines and compare the χ 2 kernel with the RBF kernel. Our results lead to a 10-fold speed increase without any loss of accuracy and to a 30-fold speed increase with 17 % loss of accuracy, where the latter system does real-time classification at 26 images per second. Categories andSubjectDescriptor