“Are Machines Better Than Humans in Image Tagging?” - A User Study Adds to the Puzzle

“Do machines perform better than humans in visual recognition tasks?” Not so long ago, this question would have been considered even somewhat provoking and the answer would have been clear: “No”. In this paper, we present a comparison of human and machine performance with respect to annotation for multimedia retrieval tasks. Going beyond recent crowdsourcing studies in this respect, we also report results of two extensive user studies. In total, 23 participants were asked to annotate more than 1000 images of a benchmark dataset, which is the most comprehensive study in the field so far. Krippendorff’s α is used to measure inter-coder agreement among several coders and the results are compared with the best machine results. The study is preceded by a summary of studies which compared human and machine performance in different visual and auditory recognition tasks. We discuss the results and derive a methodology in order to compare machine performance in multimedia annotation tasks at human level. This allows us to formally answer the question whether a recognition problem can be considered as solved. Finally, we are going to answer the initial question

Effect of blood glucose level on standardized uptake value (SUV) in F-18- FDG PET-scan : a systematic review and meta-analysis of 20,807 individual SUV measurements

Objectives To evaluate the effect of pre-scan blood glucose levels (BGL) on standardized uptake value (SUV) in F-18-FDG-PET scan. Methods A literature review was performed in the MEDLINE, Embase, and Cochrane library databases. Multivariate regression analysis was performed on individual datum to investigate the correlation of BGL with SUVmax and SUVmean adjusting for sex, age, body mass index (BMI), diabetes mellitus diagnosis, F-18-FDG injected dose, and time interval. The ANOVA test was done to evaluate differences in SUVmax or SUVmean among five different BGL groups (200 mg/dl). Results Individual data for a total of 20,807 SUVmax and SUVmean measurements from 29 studies with 8380 patients was included in the analysis. Increased BGL is significantly correlated with decreased SUVmax and SUVmean in brain (p <0.001, p <0.001,) and muscle (p <0.001, p <0.001) and increased SUVmax and SUVmean in liver (p = 0.001, p = 0004) and blood pool (p=0.008, p200 mg/dl had significantly lower SUVmax. Conclusion If BGL is lower than 200mg/dl no interventions are needed for lowering BGL, unless the liver is the organ of interest. Future studies are needed to evaluate sensitivity and specificity of FDG-PET scan in diagnosis of malignant lesions in hyperglycemia.Peer reviewe

Topic based pose relevance learning in dance archives

This paper improves spatial pyramid kernel (SPK) and proposes a relevance learning approach to compare performer's poses in a large dance archive, the NRCD collection1. Domain knowledge of Choreutics is exploited to define pose topics and a selection operator is developed for pose topic matching. The visual structure descriptor of self similarity (SSF) is extended to hierarchical self similarity (HSSF) to keep shape context. The framework of Bag-of-Visual Words (BOVW) is applied to encode as well as to speed up the matching on pose topics/topic combinations. This alleviates the complexity in limb allocation which is infeasible in our data. Extensive experiments show that the new approach outperforms the original SPK in both precision and robustness

A BOVW Based Query Generative Model

Bag-of-visual words (BOVW) is a local feature based framework for content-based image and video retrieval. Its performance relies on the discriminative power of visual vocabulary, i.e. the cluster set on local features. However, the optimisation of visual vocabulary is of a high complexity in a large collection. This paper aims to relax such a dependence by adapting the query generative model to BOVW based retrieval. Local features are directly projected onto latent content topics to create effective visual queries; visual word distributions are learnt around local features to estimate the contribution of a visual word to a query topic; the relevance is justified by considering concept distributions on visual words as well as on local features. Massive experiments are carried out the TRECVid 2009 collection. The notable improvement on retrieval performance shows that this probabilistic framework alleviates the problem of visual ambiguity and is able to afford visual vocabulary with relatively low discriminative power

PET/MRI in Oncological Imaging: State of the Art

Positron emission tomography (PET) combined with magnetic resonance imaging (MRI) is a hybrid technology which has recently gained interest as a potential cancer imaging tool. Compared with CT, MRI is advantageous due to its lack of ionizing radiation, superior soft-tissue contrast resolution, and wider range of acquisition sequences. Several studies have shown PET/MRI to be equivalent to PET/CT in most oncological applications, possibly superior in certain body parts, e.g., head and neck, pelvis, and in certain situations, e.g., cancer recurrence. This review will update the readers on recent advances in PET/MRI technology and review key literature, while highlighting the strengths and weaknesses of PET/MRI in cancer imaging