ImageCLEF 2013: The vision, the data and the open challenges

This paper presents an overview of the ImageCLEF 2013 lab. Since its first edition in 2003, ImageCLEF has become one of the key initiatives promoting the benchmark evaluation of algorithms for the cross-language annotation and retrieval of images in various domains, such as public and personal images, to data acquired by mobile robot platforms and botanic collections. Over the years, by providing new data collections and challenging tasks to the community of interest, the ImageCLEF lab has achieved an unique position in the multi lingual image annotation and retrieval research landscape. The 2013 edition consisted of three tasks: the photo annotation and retrieval task, the plant identification task and the robot vision task. Furthermore, the medical annotation task, that traditionally has been under the ImageCLEF umbrella and that this year celebrates its tenth anniversary, has been organized in conjunction with AMIA for the first time. The paper describes the tasks and the 2013 competition, giving an unifying perspective of the present activities of the lab while discussion the future challenges and opportunities.This work has been partially supported by the Halser Foundation (B. C.),by the LiMoSINe FP7 project under grant # 288024 (B. T.), by the Khresmoi (grant# 257528) and PROMISE ( grant # 258191) FP 7 projects (H.M.) and by the tranScriptorium FP7 project under grant # 600707 (M. V., R. P.)Caputo ., B.; Muller ., H.; Thomee ., B.; Villegas, M.; Paredes Palacios, R.; Zellhofer ., D.; Goeau ., H.... (2013). ImageCLEF 2013: The vision, the data and the open challenges. En Information Access Evaluation. Multilinguality, Multimodality, and Visualization. Springer Verlag (Germany). 8138:250-268. https://doi.org/10.1007/978-3-642-40802-1_26S2502688138Muller, H., Clough, P., Deselaers, T., Caputo, B.: ImageCLEF: experimental evaluation in visual information retrieval. 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β-Catenin is a pH sensor with decreased stability at higher intracellular pH.

β-Catenin functions as an adherens junction protein for cell-cell adhesion and as a signaling protein. β-catenin function is dependent on its stability, which is regulated by protein-protein interactions that stabilize β-catenin or target it for proteasome-mediated degradation. In this study, we show that β-catenin stability is regulated by intracellular pH (pHi) dynamics, with decreased stability at higher pHi in both mammalian cells and Drosophila melanogaster β-Catenin degradation requires phosphorylation of N-terminal residues for recognition by the E3 ligase β-TrCP. While β-catenin phosphorylation was pH independent, higher pHi induced increased β-TrCP binding and decreased β-catenin stability. An evolutionarily conserved histidine in β-catenin (found in the β-TrCP DSGIHS destruction motif) is required for pH-dependent binding to β-TrCP. Expressing a cancer-associated H36R-β-catenin mutant in the Drosophila eye was sufficient to induce Wnt signaling and produced pronounced tumors not seen with other oncogenic β-catenin alleles. We identify pHi dynamics as a previously unrecognized regulator of β-catenin stability, functioning in coincidence with phosphorylation

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM

COLD: The CoSy localization database

Two key competencies for mobile robotic systems are localization and semantic context interpretation. Recently, vision has become the modality of choice for these problems as it provides richer and more descriptive sensory input. At the same time, designing and testing vision-based algorithms still remains a challenge, as large amounts of carefully selected data are required to address the high variability of visual information. In this paper we present a freely available database which provides a large-scale, flexible testing environment for vision-based topological localization and semantic knowledge extraction in robotic systems. The database contains 76 image sequences acquired in three different indoor environments across Europe. Acquisition was performed with the same perspective and omnidirectional camera setup, in rooms of different functionality and under various conditions. The database is an ideal testbed for evaluating algorithms in real-world scenarios with respect to both dynamic and categorical variations

Confidence-based cue integration for visual place recognition

Abstract — 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. I