CLEF2007 Image Annotation Task: an SVM-based Cue Integration Approach

This paper presents the algorithms and results of our participation to the medical image annotation task of ImageCLEFmed 2007. We proposed, as a general strategy, a multi-cue approach where images are represented both by global and local descrip- tors, so to capture di®erent types of information. These cues are combined during the classi¯cation step following two alternative SVM-based strategies. The ¯rst algorithm, called Discriminative Accumulation Scheme (DAS), trains an SVM for each feature type, and considers as output of each classi¯er the distance from the separating hyper- plane. The ¯nal decision is taken on a linear combination of these distances: in this way cues are accumulated, thus even when they both are misleaded the ¯nal result can be correct. The second algorithm uses a new Mercer kernel that can accept as input di®erent feature types while keeping them separated. In this way, cues are selected and weighted, for each class, in a statistically optimal fashion. We call this approach Multi Cue Kernel (MCK). We submitted several runs, testing the performance of the single-cue SVM and of the two cue integration methods. Our team was called BLOOM (BLance°Or-tOMed.im2) from the name of our sponsors. The DAS algorithm obtained a score of 29.9, which ranked ¯fth among all submissions. We submitted two versions of the MCK algorithm, one using the one-vs-all multiclass extension of SVMs and the other using the one-vs-one extension. They scored respectively 26.85 and 27.54, ranking ¯rst and second among all submissions

Confidence-based Cue Integration for Visual Place Recognition

A distinctive feature of intelligent systems is their capability to analyze their level of expertise for a given task; in other words, they know what they know. As a way towards this ambitious goal, this paper presents a recognition algorithm able to measure its own level of confidence and, in case of uncertainty, to seek for extra information so to increase its own knowledge and ultimately achieve better performance. We focus on the visual place recognition problem for topological localization, and we take an SVM approach. We propose a new method for measuring the confidence level of the classification output, based on the distance of a test image and the average distance of training vectors. This method is combined with a discriminative accumulation scheme for cue integration. We show with extensive experiments that the resulting algorithm achieves better performances for two visual cues than the classic single cue SVM on the same task, while minimising the computational load. More important, our method provides a reliable measure of the level of confidence of the decision

Longitudinal Cognitive Decline in a Novel Rodent Model of Cerebral Amyloid Angiopathy Type-1

Cerebral amyloid angiopathy (CAA) is a small vessel disease characterized by β-amyloid (Aβ) accumulation in and around the cerebral blood vessels and capillaries and is highly comorbid with Alzheimer’s disease (AD). Familial forms of CAA result from mutations within the Aβ domain of the amyloid β precursor protein (AβPP). Numerous transgenic mouse models have been generated around expression of human AβPP mutants and used to study cerebral amyloid pathologies. While behavioral deficits have been observed in many AβPP transgenic mouse lines, relative to rats, mice are limited in behavioral expression within specific cognitive domains. Recently, we generated a novel rat model, rTg-DI, which expresses Dutch/Iowa familial CAA Aβ in brain, develops progressive and robust accumulation of cerebral microvascular fibrillar Aβ beginning at 3 months, and mimics many pathological features of the human disease. The novel rTg-DI model provides a unique opportunity to evaluate the severity and forms of cognitive deficits that develop over the emergence and progression of CAA pathology. Here, we present an in-depth, longitudinal study aimed to complete a comprehensive assessment detailing phenotypic disease expression through extensive and sophisticated operant testing. Cohorts of rTg-DI and wild-type (WT) rats underwent operant testing from 6 to 12 months of age. Non-operant behavior was assessed prior to operant training at 4 months and after completion of training at 12 months. By 6 months, rTg-DI animals demonstrated speed–accuracy tradeoffs that later manifested across multiple operant tasks. rTg-DI animals also demonstrated delayed reaction times beginning at 7 months. Although non-operant assessments at 4 and 12 months indicated comparable mobility and balance, rTg-DI showed evidence of slowed environmental interaction. Overall, this suggests a form of sensorimotor slowing is the likely core functional impairment in rTg-DI rats and reflects similar deficits observed in human CAA

Hashmod: A Hashing Method for Scalable 3D Object Detection

We present a scalable method for detecting objects and estimating their 3D poses in RGB-D data. To this end, we rely on an efficient representation of object views and employ hashing techniques to match these views against the input frame in a scalable way. While a similar approach already exists for 2D detection, we show how to extend it to estimate the 3D pose of the detected objects. In particular, we explore different hashing strategies and identify the one which is more suitable to our problem. We show empirically that the complexity of our method is sublinear with the number of objects and we enable detection and pose estimation of many 3D objects with high accuracy while outperforming the state-of-the-art in terms of runtime.Comment: BMVC 201