Human-interpretable and deep features for image privacy classification

Privacy is a complex, subjective and contextual concept that is difficult to define. Therefore, the annotation of images to train privacy classifiers is a challenging task. In this paper, we analyse privacy classification datasets and the properties of controversial images that are annotated with contrasting privacy labels by different assessors. We discuss suitable features for image privacy classification and propose eight privacy-specific and human-interpretable features. These features increase the performance of deep learning models and, on their own, improve the image representation for privacy classification compared with much higher dimensional deep features

Temporal validation of particle filters for video tracking

This is the author’s version of a work that was accepted for publication in Journal Computer Vision and Image Understanding. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal Computer Vision and Image Understanding, 131 (2015) DOI: 10.1016/j.cviu.2014.06.016A novel approach to determine adaptively the temporal consistency of Particle Filters.The proposed method is demonstrated on online performance evaluation of tracking.Temporal consistency is modeled by convolutions of mixtures of Gamma distributions.The proposed method does not need thresholds and can be used on large datasets. We present an approach for determining the temporal consistency of Particle Filters in video tracking based on model validation of their uncertainty over sliding windows. The filter uncertainty is related to the consistency of the dispersion of the filter hypotheses in the state space. We learn an uncertainty model via a mixture of Gamma distributions whose optimum number is selected by modified information-based criteria. The time-accumulated model is estimated as the sequential convolution of the uncertainty model. Model validation is performed by verifying whether the output of the filter belongs to the convolution model through its approximated cumulative density function. Experimental results and comparisons show that the proposed approach improves both precision and recall of competitive approaches such as Gaussian-based online model extraction, bank of Kalman filters and empirical thresholding. We combine the proposed approach with a state-of-the-art online performance estimator for video tracking and show that it improves accuracy compared to the same estimator with manually tuned thresholds while reducing the overall computational cost.This work was partially supported by the Spanish Government (EventVideo, TEC2011-25995) and by the EU Crowded Environments monitoring for Activity Understanding and Recognition (CENTAUR, FP7-PEOPLE-2012-IAPP) project under GA number 324359. Most of the work reported in this paper was done at the Centre for Intelligent Sensing in Queen Mary University of London

Cost-Aware Coalitions for Collaborative Tracking in Resource-Constrained Camera Networks

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. C. SanMiguel and A. Cavallaro, "Cost-Aware Coalitions for Collaborative Tracking in Resource-Constrained Camera Networks," in IEEE Sensors Journal, vol. 15, no. 5, pp. 2657-2668, May 2015. doi: 10.1109/JSEN.2014.2367015We propose an approach to create camera coalitions in resource-constrained camera networks and demonstrate it for collaborative target tracking. We cast coalition formation as a decentralized resource allocation process where the best cameras among those viewing a target are assigned to a coalition based on marginal utility theory. A manager is dynamically selected to negotiate with cameras whether they will join the coalition and to coordinate the tracking task. This negotiation is based not only on the utility brought by each camera to the coalition, but also on the associated cost (i.e. additional processing and communication). Experimental results and comparisons using simulations and real data show that the proposed approach outperforms related state-of-the-art methods by improving tracking accuracy in cost-free settings. Moreover, under resource limitations, the proposed approach controls the tradeoff between accuracy and cost, and achieves energy savings with only a minor reduction in accuracy.This work was supported in part by the EU Crowded Environments monitoring for Activity Understanding and Recognition (CEN-TAUR, FP7-PEOPLE-2012-IAPP) Project under GA number 324359, and in part by the Artemis JU and U.K. Technology Strategy Board as part of the Cognitive and Perceptive Cameras (COPCAMS) Project under GA number 332913

A comparative study on the impact of business model design & lean startup approach versus traditional business plan on mobile startups performance

usiness Model Design (BMD) & Lean Startup (LSA) approach are two widespread practices among entrepreneurs, where many Mobile startups declare to adopt them. However, neither of the two frameworks are well rooted in the academic literature; and few studies address the issue of whether they actually outperform traditional approaches to new Mobile Startups creation. This study's aim is to assesses the contribution to performance of the combined use of BMD and LSA for two startups operating in the highly dynamic Mobile Applications Industry; performances are then compared to those achieved by two Mobile Star-ups adopting the traditional Business Plan approach. Findings reveal how the combined use of BMD and LSA outperforms the traditional BP in the cases analyzed, thus constituting a promising methodology to support Strategic Entrepreneurship

S-adenosylmethionine and superoxide dismutase 1 synergistically counteract Alzheimer's disease features progression in tgCRND8 mice

Recent evidence emphasizes the role of dysregulated one-carbon metabolism in Alzheimer's Disease (AD). Exploiting a nutritional B-vitamin deficiency paradigm, we have previously shown that PSEN1 and BACE1 activity is modulated by one-carbon metabolism, leading to increased amyloid production. We have also demonstrated that S-adenosylmethionine (SAM) supplementation contrasted the AD-like features, induced by B-vitamin deficiency. In the present study, we expanded these observations by investigating the effects of SAM and SOD (Superoxide dismutase) association. TgCRND8 AD mice were fed either with a control or B-vitamin deficient diet, with or without oral supplementation of SAM + SOD. We measured oxidative stress by lipid peroxidation assay, PSEN1 and BACE1 expression by Real-Time Polymerase Chain Reaction (PCR), amyloid deposition by ELISA assays and immunohistochemistry. We found that SAM + SOD supplementation prevents the exacerbation of AD-like features induced by B vitamin deficiency, showing synergistic effects compared to either SAM or SOD alone. SAM + SOD supplementation also contrasts the amyloid deposition typically observed in TgCRND8 mice. Although the mechanisms underlying the beneficial effect of exogenous SOD remain to be elucidated, our findings identify that the combination of SAM + SOD could be carefully considered as co-adjuvant of current AD therapies