AMNet: Memorability Estimation with Attention

In this paper we present the design and evaluation of an end-to-end trainable, deep neural network with a visual attention mechanism for memorability estimation in still images. We analyze the suitability of transfer learning of deep models from image classification to the memorability task. Further on we study the impact of the attention mechanism on the memorability estimation and evaluate our network on the SUN Memorability and the LaMem datasets. Our network outperforms the existing state of the art models on both datasets in terms of the Spearman's rank correlation as well as the mean squared error, closely matching human consistency

Three IoT Wearables in Six European Cities! Reality and Perception

In the last decade, the Internet of Things (IoT) technology has attracted lots of attention. This paper evaluates the impact of three different IoT technologies represented in three types of wearables; Smart Glasses, Tracking Devices, and Crowd and Staff Wristbands. The deployment of these devices took place in a number of cultural and sports events in six European counties, as part of the European Commission funded project; MONICA. The analysis focused on the usability of the wearables and their impact on safety and security

Ear Recognition using Chainlet based Multi-Band SVM

This paper presents a Chainlet based Multi-Band Ear Recognition using Support Vector Machine (CMBER-SVM) algorithm. The proposed method divides the gray input image into a number of bands based on the intensity of its pixels, resembling a hyperspectral image. It then applies Canny edge detection on each resulting normalized band, extracting edges that represent the ear pattern in each band. The resulting binary edge maps are then flattened, generating a single binary edge map. This edge map is then split into non-overlapping cells and the Freeman chain code for each group of connected edges within each cell is calculated. A histogram of each group of contiguous four cells is calculated, and the results histograms are then normalized and concatenated to form a chainlet for the input image. The resulting chainlet histogram vectors of the images of the dataset are then used for training and testing a pairwise Support Vector Machine (SVM). Experimental results on images of two benchmark ear image datasets show that the proposed CMBER-SVM technique outperforms both the state of the art statistical and learning based ear recognition methods. Index Terms—ear recognition, chainlets, support vector machine, multi-band image generatio

Combining machine learning and metaheuristics algorithms for classification method PROAFTN

© Crown 2019. The supervised learning classification algorithms are one of the most well known successful techniques for ambient assisted living environments. However the usual supervised learning classification approaches face issues that limit their application especially in dealing with the knowledge interpretation and with very large unbalanced labeled data set. To address these issues fuzzy classification method PROAFTN was proposed. PROAFTN is part of learning algorithms and enables to determine the fuzzy resemblance measures by generalizing the concordance and discordance indexes used in outranking methods. The main goal of this chapter is to show how the combined meta-heuristics with inductive learning techniques can improve performances of the PROAFTN classifier. The improved PROAFTN classifier is described and compared to well known classifiers, in terms of their learning methodology and classification accuracy. Through this chapter we have shown the ability of the metaheuristics when embedded to PROAFTN method to solve efficiency the classification problems