Giorgio Roffo

In the last decade, new ways of shopping online have increased the possibility of buying products and services more easily and faster than ever. In this new context, personality is a key determinant in the decision making of the consumer when shopping. A person's buying choices are influenced by psychological factors like impulsiveness; indeed some consumers may be more susceptible to making impulse purchases than others. Since affective metadata are more closely related to the user's… Visualizza altro

In the last decade, new ways of shopping online have increased the possibility of buying products and services more easily and faster than ever. In this new context, personality is a key determinant in the decision making of the consumer when shopping. A person's buying choices are influenced by psychological factors like impulsiveness; indeed some consumers may be more susceptible to making impulse purchases than others. Since affective metadata are more closely related to the user's experience than generic parameters, accurate predictions reveal important aspects of user's attitudes, social life, including attitude of others and social identity. This work proposes a highly innovative research that uses a personality perspective to determine the unique associations among the consumer's buying tendency and advert recommendations. In fact, the lack of a publicly available benchmark for computational advertising do not allow both the exploration of this intriguing research direction and the evaluation of recent algorithms. We present the ADS Dataset, a publicly available benchmark consisting of 300 real advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated by 120 unacquainted individuals, enriched with Big-Five users' personality factors and 1,200 personal users' pictures. Meno dettagli

In an era where accumulating data is easy and storing it inexpensive, feature selection plays a central role in helping to reduce the high-dimensionality of huge amounts of otherwise meaningless data. In this paper, we propose a graph-based method for feature selection that ranks features by identifying the most important ones into arbitrary set of cues. Mapping the problem on an affinity graph - where features are the nodes - the solution is given by assessing the importance of nodes through… Visualizza altro

In an era where accumulating data is easy and storing it inexpensive, feature selection plays a central role in helping to reduce the high-dimensionality of huge amounts of otherwise meaningless data. In this paper, we propose a graph-based method for feature selection that ranks features by identifying the most important ones into arbitrary set of cues. Mapping the problem on an affinity graph - where features are the nodes - the solution is given by assessing the importance of nodes through some indicators of centrality, in particular, the \emph{Eigenvector Centrality (EC)}. The gist of EC is to estimate the importance of a feature as a function of the importance of its neighbors. Ranking central nodes individuates candidate features, which turn out to be effective from a classification point of view, as proved by a thoroughly experimental section. Our approach has been tested on 7 diverse datasets from recent literature (e.g., biological data, object recognition, among others), and compared against filter, embedded, and wrappers methods. The results are remarkable in terms of accuracy, stability and low execution time. Meno dettagli

Object tracking is one of the most important tasks in many applications of computer vision. Many tracking methods use a fixed set of features ignoring that appearance of a target object may change drastically due to intrinsic and extrinsic factors. The ability to dynamically identify discriminative features would help in handling the appearance variability by improving tracking performance. The contribution of this work is threefold. Firstly, this paper presents a collection of several modern… Visualizza altro

Object tracking is one of the most important tasks in many applications of computer vision. Many tracking methods use a fixed set of features ignoring that appearance of a target object may change drastically due to intrinsic and extrinsic factors. The ability to dynamically identify discriminative features would help in handling the appearance variability by improving tracking performance. The contribution of this work is threefold. Firstly, this paper presents a collection of several modern feature selection approaches selected among filter, embedded, and wrapper methods. Secondly, we provide extensive tests regarding the classification task intended to explore the strengths and weaknesses of the proposed methods with the goal to identify the right candidates for online tracking. Finally, we show how feature selection mechanisms can be successfully employed for ranking the features used by a tracking system, maintaining high frame rates. In particular, feature selection mounted on the Adaptive Color Tracking (ACT) system operates at over 110 FPS. This work demonstrates the importance of feature selection in online and realtime applications, resulted in what is clearly a very impressive performance, our solutions improve by 3% up to 7% the baseline ACT while providing superior results compared to 29 state-of-the-art tracking methods. Meno dettagli

Filter-based feature selection has become crucial in many classification settings, especially object recognition, recently faced with feature learning strategies that originate thousands of cues. In this paper, we propose a feature selection method exploiting the convergence properties of power series of matrices, and introducing the concept of infinite feature selection (Inf-FS). Considering a selection of features as a path among feature distributions and letting these paths tend to an… Visualizza altro

Filter-based feature selection has become crucial in many classification settings, especially object recognition, recently faced with feature learning strategies that originate thousands of cues. In this paper, we propose a feature selection method exploiting the convergence properties of power series of matrices, and introducing the concept of infinite feature selection (Inf-FS). Considering a selection of features as a path among feature distributions and letting these paths tend to an infinite number permits the investigation of the importance (relevance and redundancy) of a feature when injected into an arbitrary set of cues. Ranking the importance individuates candidate features, which turn out to be effective from a classification point of view, as proved by a thoroughly experimental section. The Inf-FS has been tested on thirteen diverse benchmarks, comparing against filters, embedded methods, and wrappers; in all the cases we achieve top performances, notably on the classification tasks of PASCAL VOC 2007-2012.

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Interacting via text chats can be considered as a hybrid type of communication, in which textual information delivery follows turn-taking dynamics, resembling spoken interactions. An interesting research question is whether personality can be observed in chats, similarly as happening in face-to-face exchanges. After an encouraging preliminary work on Skype, in this study we have set up our own chat service in which key-logging functionalities have been activated, so that the timings of each key… Visualizza altro

Interacting via text chats can be considered as a hybrid type of communication, in which textual information delivery follows turn-taking dynamics, resembling spoken interactions. An interesting research question is whether personality can be observed in chats, similarly as happening in face-to-face exchanges. After an encouraging preliminary work on Skype, in this study we have set up our own chat service in which key-logging functionalities have been activated, so that the timings of each key pressing can be measured. Using this framework, we organized semi-structured chats between 50 subjects, whose personality traits have been analyzed through psychometric tests, and a single operator, for a total of 16 hours of conversation. On this data, we have observed that some personality traits are linked with the way we are chatting (measured by stylometric cues), by means of statistically significant correlations and regression studies. Finally, we have assessed that some of the stylometric cues are very discriminative for the recognition of a user in a identification scenario. These facts taken together could underlie that some personality traits drive us in chatting in a particular fashion, which turns out to be very recognizable. Meno dettagli

Text chatting represents a hybrid type of communication, where textual information is delivered following turn-taking dynamics, which characterize spoken interactions. It is interesting to understand whether special interactional behavior can emerge in chats, similarly as it does in face-to-face exchanges. In this work, we focus on the writing style of individuals, analyzing how it can be recognized given a portion of chat, and how personality comes into play in this scenario. Two
important… Visualizza altro

Text chatting represents a hybrid type of communication, where textual information is delivered following turn-taking dynamics, which characterize spoken interactions. It is interesting to understand whether special interactional behavior can emerge in chats, similarly as it does in face-to-face exchanges. In this work, we focus on the writing style of individuals, analyzing how it can be recognized given a portion of chat, and how personality comes into play in this scenario. Two
important results do emerge: 1) some traits correlate signi?cantly with some characteristics of people's chatting style, captured by stylometric features; 2) some of such features are very e?ective in recognizing a person among a gallery of diverse individuals. This seems to suggest that some personality traits may lead people to chat with a particular style, which turns out to be very recognizable. For example, motor impulsiveness gives a signi?cative (negative) correlation with the use of the suspension points (. . . ), that is also one of the most discriminative characteristics in chats. This and other relations clearly emerge on a dataset on 45 subjects, monitored for 3 months, whose personality traits have been analyzed through self-administered questionnaires. What turns out is that chatting is de?nitely more than just typing. Meno dettagli

Identity safekeeping on chats has recently become an important problem on social networks. One of the most important issues is identity theft, where impostors steal the identity of a person, substituting her in the chats, in order to have access to private information. In the literature, the problem has been addressed by designing sets of features which capture the way a person interacts through the chats.
However, such approaches perform well only on the long term, after a long conversation… Visualizza altro

Identity safekeeping on chats has recently become an important problem on social networks. One of the most important issues is identity theft, where impostors steal the identity of a person, substituting her in the chats, in order to have access to private information. In the literature, the problem has been addressed by designing sets of features which capture the way a person interacts through the chats.
However, such approaches perform well only on the long term, after a long conversation has been performed; this is a problem, since in the early turns of a conversation, much important information can be stolen. This paper focuses on this issue, presenting a learning approach which boosts the performance of user recognition and verification, allowing to recognize a subject with considerable accuracy. The proposed method is based on a recent framework of one-shot multi-class multi-view learning, based on Reproducing Kernel Hilbert Spaces (RKHS) theory. Our technique reaches a recognition rate of 76.9% in terms of AUC of the Cumulative Matching Characteristic curve, with only 10 conversational turns considered, on a total of 78 subjects. This sets the new best performances on a public conversation benchmark. Meno dettagli

Identity safekeeping has recently become an important problem for the social web: as case of study, we focus here on instant messaging platforms, proposing novel softbiometric cues for user recognition and verification. Specifically, we design a set of features encoding effectively how a person converses: since chats are crossbreeds of written text and face-to-face verbal communication, the features inherit
equally from textual authorship attribution and conversational analysis of speech… Visualizza altro

Identity safekeeping has recently become an important problem for the social web: as case of study, we focus here on instant messaging platforms, proposing novel softbiometric cues for user recognition and verification. Specifically, we design a set of features encoding effectively how a person converses: since chats are crossbreeds of written text and face-to-face verbal communication, the features inherit
equally from textual authorship attribution and conversational analysis of speech. Importantly, our cues ignore completely the semantics of the chat, relying solely on non-verbal aspects, taking care of possible privacy and ethical issues. We apply our approach on a novel dataset of 94 different individuals, whose chat conversations have been recorded for an average period of five months; recognition rate, intended as normalized AUC on CMC curve, is 95.73%, while verification rate amounts to 95.66%, as normalized AUC on ROC curve. Meno dettagli

Authorship attribution (AA) aims at recognizing automatically the author of a given text sample. Traditionally applied to literary texts, AA faces now the new challenge of recognizing the identity of people involved in chat conversations. These share many aspects with spoken conversations, but AA approaches did not take it into account so far. Hence, this paper tries to fill the gap and proposes two novelties that improve the effectiveness of traditional AA approaches for this type of data: the… Visualizza altro

Authorship attribution (AA) aims at recognizing automatically the author of a given text sample. Traditionally applied to literary texts, AA faces now the new challenge of recognizing the identity of people involved in chat conversations. These share many aspects with spoken conversations, but AA approaches did not take it into account so far. Hence, this paper tries to fill the gap and proposes two novelties that improve the effectiveness of traditional AA approaches for this type of data: the first is to adopt features inspired by Conversation Analysis (in particular for turn-taking), the second is to extract the features from individual turns rather than from entire conversations. The experiments have been performed over a corpus of dyadic chat conversations (77 individuals in total). The performance in identifying the persons involved in each exchange, measured in terms of area under the Cumulative Match Characteristic curve, is 89.5%. Meno dettagli

We propose an optimized visual tracking algorithm based on the real-time selection of locally and temporally discriminative features. A novel feature selection mechanism is embedded in the Adaptive Color Names (CN) tracking system that adaptively selects the top-ranked discriminative features for tracking. The Dynamic Feature Selection Tracker (DFST) provides a significant gain in accuracy and precision allowing the use of a dynamic set of features that results in an increased system… Visualizza altro

We propose an optimized visual tracking algorithm based on the real-time selection of locally and temporally discriminative features. A novel feature selection mechanism is embedded in the Adaptive Color Names (CN) tracking system that adaptively selects the top-ranked discriminative features for tracking. The Dynamic Feature Selection Tracker (DFST) provides a significant gain in accuracy and precision allowing the use of a dynamic set of features that results in an increased system flexibility. Our ranking solution is based on the Inf-FS. The Inf-FS is an unsupervised method, it ranks features according with their “redundancy” (without using class labels). For the sake of foreground/background separation, we propose a supervised variant that is able to score high features with respect to class “relevancy”, that is, how well each feature discriminates between foreground (target) and background. Therefore, we design the input adjacency matrix of the Inf-FS in a supervised manner by significantly reducing the time needed for building the graph. The CN tracking system does not fit the size of the bounding box. Indeed, in the original framework, the bounding box remains of the same size during the tracking process. We propose a simple yet effective way of adapting the size of the box by using a fast online algorithm for learning dictionaries. At each update, we use multiple examples around the target (at different positions and scales), we find tight bounding boxes enclosing the target by selecting the one that minimizes the reconstruction error. Thus, we also improved the CN by adding micro-shift at the predicted position and bounding box adaptation. Meno dettagli