UNED @ Retrieving Diverse Social Images Task

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This paper summarizes the participation of UNED at the 2014 Retrieving Diverse Social Images Task [3]. We propose a novel approach based on Formal Concept analysis (FCA) to detect the latent topics related to the images and a later Hierarchical Agglomerative Clustering (HAC) to put together the images according to these latent topics. The diversification will be based on offering images from the different topics detected. In order to detect these latent topics, two kinds of data have been tested: only information related to the description of the images and all the textual information related to the images. The results show that our proposal is suitable for search result diversification, achieving similar results to those in the state of the art. http://ceur-ws.org/Vol-1263/mediaeval2014_submission_9.pdf

Software

UNED @ Retrieving Diverse Social Images Task
A. Castellanos, Ana García-Serrano, J. Cigarrán
ETSI Infomática, UNED
MediaEval 2014 Workshop,
Barcelona, October 16-17, 2014.

STEP1: Kullback Leibler Divergence (KLD) technique to extract the most relevant terms “different and original”.
STEP 2: The Formal Concept Analysis (FCA) technique to organize the images according to their shared attributes into a generalization-specification relationship (lattice).
2
TBIR: Four steps approach

3
TBIR: Four steps approach
STEP 2: The Formal Concept Analysis (FCA) technique generates a large number of nodes in the lattice
STEP 3: A Hierarchical Agglomerative Clustering (HAC) approach to the re-grouping the lattice nodes (maximizing diversity):
–Single Linking-based Hierarchical Clustering to cluster together similar formal concepts using as similarity measure for the clustering the Zero-Induces Index

4
UNED@Retrieving Diverse
STEP 4: Final image selection and re-ranking
Conclusions:
–CBIR for STEP 4 (real time on 50?)
–Results ¿stability reached?
Run2
Run5
F1@5
0,2546
0,2493
F1@10
0,3932
0,3882
F1@20
0,5502
0,5429
F1@30
0,6256
0,6267
F1@40
0,6359
0,6646
F1@50
0,6051
0,6814

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A Temporal-Spatial Attention Model for Medical Image Detection

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper20.pdf YouTube: https://youtu.be/CVelQl5Luf0 Quoc-Huy Trinh, Minh-Van Nguyen, Thiet-Gia Huynh and Minh-Triet Tran : HCMUS-Juniors 2020 at Medico Task in MediaEval 2020: Refined Deep Neural Network and UNet for Polyps Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online. The Medico: Multimedia Task focuses on developing an efficient and accurate framework to computer-aided diagnosis systems for automatic polyp segmentation to detect all types of polyps in endoscopic images of the gastrointestinal (GI) tract. We are HCMUS-team approach a solution, which includes combination Residual module, Inception module, Adaptive Convolutional neural network with Unet model and PraNet to semantic segmentation all types of polyps in endoscopic images. We submit multiple runs with different architecture and parameters in our model. Our methods show potential results in accuracy and efficiency through multiple experiments.

HCMUS-Juniors 2020 at Medico Task in MediaEval 2020: Refined Deep Neural Netw...

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper15.pdf Rabindra Khadka : Transfer of Knowledge: Fine-tuning for Polyp Segmentation with Attention. Proc. of MediaEval 2020, 14-15 December 2020, Online. This paper describes how the transfer of prior knowledge can effectively take on segmentation tasks with the help of attention mechanisms. The UNet model pretrained on brain MRI dataset was fine-tuned with the polyp dataset. Attention mechanism was integrated to focus on relevant regions in the input images. The implemented architecture is evaluated on 200 validation images based on intersection over union and dice score between groundtruth and predicted region. The model demonstrates a promising result with computational efciency.

Fine-tuning for Polyp Segmentation with Attention

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper12.pdf Adrian Krenzer and Frank Puppe : Bigger Networks are not Always Better: Deep Convolutional Neural Networks for Automated Polyp Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online. This paper presents our team's (AI-JMU) approach to the Medico automated polyp segmentation challenge. We consider deep convolutional neural networks to be well suited for this task. To determine the best architecture we test and compare state of the art backbones and two different heads. Finally we achieve a Jaccard index of 73.74\% on the challenge test set. We further demonstrate that bigger networks do not always perform better. However the growing network size always increases the computational complexity.

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multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper51.pdf Amel Ksibi, Amina Salhi, Ala Alluhaidan and Sahar A. El-Rahman : Insights for wellbeing: Predicting Personal Air Quality Index using Regression Approach. Proc. of MediaEval 2020, 14-15 December 2020, Online. Providing air pollution information to individuals enables them to understand the air quality of their living environments. Thus, the association between people’s wellbeing and the properties of the surrounding environment is an essential area of investigation. This paper proposes Air Quality Prediction through harvesting public/open data and leveraging them to get the Personal Air Quality index. These are usually incomplete. To cope with the problem of missing data, we applied the KNN imputation method. To predict Personal Air Quality Index, we apply a voting regression approach based on three base regressors which are Gradient Boosting regressor, Random Forest regressor, and linear regressor. Evaluating the experimental results using the RMSE metric, we got an average score of 35.39 for Walker and 51.16 for Car.

Insights for wellbeing: Predicting Personal Air Quality Index using Regressio...

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper40.pdf YouTube: https://youtu.be/SL5Hvu1mARY Trung-Quan Nguyen, Dang-Hieu Nguyen and Loc Tai Tan Nguyen : Use Visual Features From Surrounding Scenes to Improve Personal Air Quality Data Prediction Performance. Proc. of MediaEval 2020, 14-15 December 2020, Online. In this paper, we propose a method to predict the personal air quality index in an area by using the combination of the levels of the following pollutants: PM2.5, NO2, and O3, measured from the nearby weather stations of that area, and the photos of surrounding scenes taken at that area. Our approach uses the Inverse Distance Weighted (IDW) technique to estimate the missing air pollutant levels and then use regression to integrate visual features from taken photos to optimize the predicted values. After that, we can use those values to calculate the Air Quality Index (AQI). The results show that the proposed method may not improve the performance of the prediction in some cases.

Use Visual Features From Surrounding Scenes to Improve Personal Air Quality ...

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1. UNED @ Retrieving Diverse Social Images Task A. Castellanos, Ana García-Serrano, J. Cigarrán ETSI Infomática, UNED MediaEval 2014 Workshop, Barcelona, October 16-17, 2014.

2. STEP1: Kullback Leibler Divergence (KLD) technique to extract the most relevant terms “different and original”. STEP 2: The Formal Concept Analysis (FCA) technique to organize the images according to their shared attributes into a generalization-specification relationship (lattice). 2 TBIR: Four steps approach

3. 3 TBIR: Four steps approach STEP 2: The Formal Concept Analysis (FCA) technique generates a large number of nodes in the lattice STEP 3: A Hierarchical Agglomerative Clustering (HAC) approach to the re-grouping the lattice nodes (maximizing diversity): –Single Linking-based Hierarchical Clustering to cluster together similar formal concepts using as similarity measure for the clustering the Zero-Induces Index

4. 4 UNED@Retrieving Diverse STEP 4: Final image selection and re-ranking Conclusions: –CBIR for STEP 4 (real time on 50?) –Results ¿stability reached? Run2 Run5 F1@5 0,2546 0,2493 F1@10 0,3932 0,3882 F1@20 0,5502 0,5429 F1@30 0,6256 0,6267 F1@40 0,6359 0,6646 F1@50 0,6051 0,6814

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