Mobile Interface for Content-Based Image Management

People make more and more use of digital image acquisition devices to capture screenshots of their everyday life. The growing number of personal pictures raise the problem of their classification. Some of the authors proposed an automatic technique for personal photo album management dealing with multiple aspects (i.e., people, time and background) in a homogenous way. In this paper we discuss a solution that allows mobile users to remotely access such technique by means of their mobile phones, almost from everywhere, in a pervasive fashion. This allows users to classify pictures they store on their devices. The whole solution is presented, with particular regard to the user interface implemented on the mobile phone, along with some experimental results

Is text preprocessing still worth the time? A comparative survey on the influence of popular preprocessing methods on Transformers and traditional classifiers

With the advent of the modern pre-trained Transformers, the text preprocessing has started to be neglected and not specifically addressed in recent NLP literature. However, both from a linguistic and from a computer science point of view, we believe that even when using modern Transformers, text preprocessing can significantly impact on the performance of a classification model. We want to investigate and compare, through this study, how preprocessing impacts on the Text Classification (TC) performance of modern and traditional classification models. We report and discuss the preprocessing techniques found in the literature and their most recent variants or applications to address TC tasks in different domains. In order to assess how much the preprocessing affects classification performance, we apply the three top referenced preprocessing techniques (alone or in combination) to four publicly available datasets from different domains. Then, nine machine learning models – including modern Transformers – get the preprocessed text as input. The results presented show that an educated choice on the text preprocessing strategy to employ should be based on the task as well as on the model considered. Outcomes in this survey show that choosing the best preprocessing technique – in place of the worst – can significantly improve accuracy on the classification (up to 25%, as in the case of an XLNet on the IMDB dataset). In some cases, by means of a suitable preprocessing strategy, even a simple Naïve Bayes classifier proved to outperform (i.e., by 2% in accuracy) the best performing Transformer. We found that Transformers and traditional models exhibit a higher impact of the preprocessing on the TC performance. Our main findings are: (1) also on modern pre-trained language models, preprocessing can affect performance, depending on the datasets and on the preprocessing technique or combination of techniques used, (2) in some cases, using a proper preprocessing strategy, simple models can outperform Transformers on TC tasks, (3) similar classes of models exhibit similar level of sensitivity to text preprocessing

Video Object Recognition and Modeling by SIFT Matching Optimization

In this paper we present a novel technique for object modeling and object recognition in video. Given a set of videos containing 360 degrees views of objects we compute a model for each object, then we analyze short videos to determine if the object depicted in the video is one of the modeled objects. The object model is built from a video spanning a 360 degree view of the object taken against a uniform background. In order to create the object model, the proposed techniques selects a few representative frames from each video and local features of such frames. The object recognition is performed selecting a few frames from the query video, extracting local features from each frame and looking for matches in all the representative frames constituting the models of all the objects. If the number of matches exceed a fixed threshold the corresponding object is considered the recognized objects .To evaluate our approach we acquired a dataset of 25 videos representing 25 different objects and used these videos to build the objects model. Then we took 25 test videos containing only one of the known objects and 5 videos containing only unknown objects. Experiments showed that, despite a significant compression in the model, recognition results are satisfactory

Object Recognition and Modeling Using SIFT Features

In this paper we present a technique for object recognition and modelling based on local image features matching. Given a complete set of views of an object the goal of our technique is the recognition of the same object in an image of a cluttered environment containing the object and an estimate of its pose. The method is based on visual modeling of objects from a multi-view representation of the object to recognize. The first step consists of creating object model, selecting a subset of the available views using SIFT descriptors to evaluate image similarity and relevance. The selected views are then assumed as the model of the object and we show that they can effectively be used to visually represent the main aspects of the object. Recognition is done making comparison between the image containing an object in generic position and the views selected as object models. Once an object has been recognized the pose can be estimated searching the complete set of views of the object. Experimental results are very encouraging using both a private dataset we acquired in our lab and a publicly available dataset

Detection of Hate Speech Spreaders using convolutional neural networks

In this paper we describe a deep learning model based on a Convolutional Neural Network (CNN). The model was developed for the Profiling Hate Speech Spreaders (HSSs) task proposed by PAN 2021 organizers and hosted at the 2021 CLEF Conference. Our approach to the task of classifying an author as HSS or not (nHSS) takes advantage of a CNN based on a single convolutional layer. In this binary classification task, on the tests performed using a 5-fold cross validation, the proposed model reaches a maximum accuracy of 0.80 on the multilingual (i.e., English and Spanish) training set, and a minimum loss value of 0.51 on the same set. As announced by the task organizers, the trained model presented is able to reach an overall accuracy of 0.79 on the full test set. This overall accuracy is obtained averaging the accuracy achieved by the model on both languages. In particular, with regard to the Spanish test set, the organizers announced that our model achieves an accuracy of 0.85, while on the English test set the same model achieved - as announced by the organizers too - an accuracy of 0.73. Thanks to the model presented in this paper, our team won the 2021 PAN competition on profiling HSSs

Combining Top-down and Bottom-up Visual Saliency for Firearms Localization

Object detection is one of the most challenging issues for computer vision researchers. The analysis of the human visual attention mechanisms can help automatic inspection systems, in order to discard useless information and improving performances and efficiency. In this paper we proposed our attention based method to estimate firearms position in images of people holding firearms. Both top-down and bottom-up mechanisms are involved in our system. The bottom-up analysis is based on a state-of-the-art approach. The top-down analysis is based on the construction of a probabilistic model of the firearms position with respect to the people\u2019s face position. This model has been created by analyzing information from of a public available database of movie frames representing actors holding firearms

A dataset of annotated omnidirectional videos for distancing applications

Omnidirectional (or 360◦ ) cameras are acquisition devices that, in the next few years, could have a big impact on video surveillance applications, research, and industry, as they can record a spherical view of a whole environment from every perspective. This paper presents two new contributions to the research community: the CVIP360 dataset, an annotated dataset of 360◦ videos for distancing applications, and a new method to estimate the distances of objects in a scene from a single 360◦ image. The CVIP360 dataset includes 16 videos acquired outdoors and indoors, annotated by adding information about the pedestrians in the scene (bounding boxes) and the distances to the camera of some points in the 3D world by using markers at fixed and known intervals. The proposed distance estimation algorithm is based on geometry facts regarding the acquisition process of the omnidirectional device, and is uncalibrated in practice: the only required parameter is the camera height. The proposed algorithm was tested on the CVIP360 dataset, and empirical results demonstrate that the estimation error is negligible for distancing applications

Enhanced P2P Services Providing Multimedia Content

The retrieval facilities of most Peer-to-Peer (P2P) systems are limited to queries based on unique identifiers or small sets of keywords. Unfortunately, this approach is very inadequate and inefficient when a huge amount of multimedia resources is shared. To address this major limitation, we propose an original image and video sharing system, in which a user is able to interactively search interesting resources by means of content-based image and video retrieval techniques. In order to limit the network traffic load, maximizing the usefulness of each peer contacted in the query process, we also propose the adoption of an adaptive overlay routing algorithm, exploiting compact representations of the multimedia resources shared by each peer. Experimental results confirm the validity of the proposed approach, that is capable of dynamically adapting the network topology to peer interests, on the basis of query interactions among users

INCIDENCE OF PSYCHOTIC DISORDERS IN PALERMO: PRELIMINARY DATA

Background: The incidence of psychotic disorders varies in different geographical areas (McGrath 2004). Recent data suggest that the incidence is higher in males, migrant minorities and in urban areas. There aren\u2019t many available epidemiological data on the incidence of psychotic disorders in Italy. This is the first incidence study on psychotic disorders carried out in Palermo, the capital of Sicily. Methods: we screened all patients presenting with their first episode of psychosis to the mental health services of our catchment area (5 inpatient, 5 outpatient units and 3 private psychiatric hospitals) over a period of three years (2008-2011). The diagnosis of psychosis was defined using the Schedules for Clinical Assessment in Neuropsychiatry (SCAN Wing, J. K., et al., 1990).The main socio-demographic data were collected using the MRC Social Data Schedule. When subjects were not available (did not consent) for interview, information was collected from clinical notes. The population at risk referred to the people aged from 18-65 who were resident in the same catchment area (Palermo Municipality) in the period considered, according to the data of the Statistic Office of Palermo Municipality). Results: we identified 216 patients affected by a first episode of psychosis (FEP): 135 M (62.5%) and 81 F (37.5%), mean age 31.42 years (SD: 11.44). 77.1% of FEP had a diagnosis of non affective psychosis, 12.8% of affective psychosis and 10.1% received a diagnosis of other psychosis. 204 subjects were Caucasian, 12 non Caucasian belonging to various ethnicities and they were all first generation migrants (4 Indian, 3 African, 2 Bangladeshi, and 3 Mixed). Population at risk is 425.194 people. The mean age of onset was lower in men than women M: 29.98 years (SD: 10.41) vs. F: 34.28 (SD:12.64) (p=0.013)The incidence of psychotic disorders in our catchment area is 16,9 per 100.000 person years. It was higher in men 21,9 per 100.000 than women 12,2 per 100.000. Discussion: Our study is the first epidemiological study in Sicily investigating the incidence of psychotic disorders. In our population men have a higher incidence of psychotic disorders than women and an earlier age of onset

The occurrence of diseases and related factors in a center for asylum seekers in italy

Introduction. Italy is the main recipient of asylum seekers in the European region, and Sicily is their first point of arrival. This geographical position creates a large job for Health Authorities to identify and deal with the health of immigrants. This study evaluates the prevalence of disease among asylum seekers, assessing which are associated factors. Methods. A cross-sectional study was conducted to analyse demographic and clinical data in an Acceptance Centres for Asylum Seekers from February 2012 to May 2013. All variables that were found to be significant on unvariable analysis for the most frequent pathologies were included in a multivariable logistic regression model. Results. Post-traumatic stress disorders with 17.4% and major depression with 7.3% were the most frequent diseases. The factors associated with post-traumatic stress disorders among asylum seekers were: major depression diagnosis (OR=2.91, p=0.004),Pakistan as a country of origin (OR=3.88, p<0.001), the largest number of medical visits (OR=1.02, p=0.033) and refugee status (OR=1.97, p=0.036). The variables linked with the diagnosis of major depression from the multivariable analysis were: suffering from post-traumatic stress disorders (OR=3.83, p<0.001), Pakistan as a country of origin (OR=3.45, p=0.004) and the highest number of visits to psychologist (OR=1.15, p<0.001). Conclusions.The mental wellbeing of asylum seekers needs special attention, and interventions should be done to prevent the consolidation of psychiatric morbidity. A short psychological screening after the arrival might prove helpful here. Moreover, carefully designed longitudinal studies should be carried out when political recommendations try to change the organization of psychological and healthcare services