Modeling Efficient Classification as a Process of Confidence Assessment and Delegation

In visual object detection and recognition, classifiers have two interesting characteristics: accuracy and speed. Accuracy depends on the complexity of the image features and classifier decision surfaces. Speed depends on the hardware and the computational effort required to use the features and decision surfaces. When attempts to increase accuracy lead to increases in complexity and effort, it is necessary to ask how much are we willing to pay for increased accuracy. For example, if increased computational effort implies quickly diminishing returns in accuracy, then those designing inexpensive surveillance applications cannot aim for maximum accuracy at any cost. It becomes necessary to find trade-offs between accuracy and effort. We study efficient classification of images depicting real-world objects and scenes. Classification is efficient when a classifier can be controlled so that the desired trade-off between accuracy and effort (speed) is achieved and unnecessary computations are avoided on a per input basis. A framework is proposed for understanding and modeling efficient classification of images. Classification is modeled as a tree-like process. In designing the framework, it is important to recognize what is essential and to avoid structures that are narrow in applicability. Earlier frameworks are lacking in this regard. The overall contribution is two-fold. First, the framework is presented, subjected to experiments, and shown to be satisfactory. Second, certain unconventional approaches are experimented with. This allows the separation of the essential from the conventional. To determine if the framework is satisfactory, three categories of questions are identified: trade-off optimization, classifier tree organization, and rules for delegation and confidence modeling. Questions and problems related to each category are addressed and empirical results are presented. For example, related to trade-off optimization, we address the problem of computational bottlenecks that limit the range of trade-offs. We also ask if accuracy versus effort trade-offs can be controlled after training. For another example, regarding classifier tree organization, we first consider the task of organizing a tree in a problem-specific manner. We then ask if problem-specific organization is necessary.

Chromatographic separation, fractionation and oxidation of selected beta-lactoglobulin peptides

The literature review elucidates the mechanism of oxidation in proteins and amino acids and gives an overview of the detection and analysis of protein oxidation products as well as information about ?-lactoglobulin and studies carried out on modifications of this protein under certain conditions. The experimental research included the fractionation of the tryptic peptides of ?-lactoglobulin using preparative-HPLC-MS and monitoring the oxidation process of these peptides via reverse phase-HPLC-UV. Peptides chosen to be oxidized were selected with respect to their amino acid content which were susceptible to oxidation and fractionated according to their m/z values. These peptides were: IPAVFK (m/z 674), ALPMHIR (m/z 838), LIVTQTMK (m/z 934) and VLVLDTDYK (m/z 1066). Even though it was not possible to solely isolate the target peptides due to co-elution of various fractions, the percentages of target peptides in the samples were satisfactory to carry out the oxidation procedure. IPAVFK and VLVLDTDYK fractions were found to yield the oxidation products reviewed in literature, however, unoxidized peptides were still present in high amounts after 21 days of oxidation. The UV data at 260 and 280 nm enabled to monitor both the main peptides and the oxidation products due to the absorbance of aromatic side-chains these peptides possess. ALPMHIR and LIVTQTMK fractions were oxidatively consumed rapidly and oxidation products of these peptides were observed even on day 0. High rates of depletion of these peptides were acredited to the presence of His (H) and sulfur-containing side-chains of Met (M). In conclusion, selected peptides hold the potential to be utilized as marker peptides in ?-lactoglobulin oxidation.