Alceu Ferraz Costa

An Efficient Algorithm for Fractal Analysis of Textures

In this paper we propose a new and efficient texture feature extraction method: the Segmentation-based Fractal Texture Analysis, or SFTA. The extraction algorithm consists in decomposing the input image into a set of binary images from which the fractal dimensions of the resulting regions are computed in order to describe segmented texture patterns. The decomposition of the input image is achieved by the Two-Threshold Binary Decomposition (TTBD) algorithm, which we also propose in this work. We evaluated SFTA for the tasks of content-based image retrieval (CBIR) and image classification, comparing its performance to that of other widely employed feature extraction methods such as Haralick and Gabor filter banks. SFTA achieved higher precision and accuracy for CBIR and image classification. Additionally, SFTA was at least 3.7 times faster than Gabor and 1.6 times faster than Haralick with respect to feature extraction time.

RSC: Mining and Modeling Temporal Activity in Social Media

Can we identify patterns of temporal activities caused by human communications in social media? Is it possible to model these patterns and tell if a user is a human or a bot based only on the timing of their postings? Social media services allow users to make postings, generating large datasets of human activity time-stamps. In this paper we analyze time-stamp data from social media services and find that the distribution of postings inter-arrival times (IAT) is characterized by four patterns: (i) positive correlation between consecutive IATs, (ii) heavy tails, (iii) periodic spikes and (iv) bimodal distribution. Based on our findings, we propose Rest-Sleep-and-Comment (RSC), a generative model that is able to match all four discovered patterns. We demonstrate the utility of RSC by showing that it can accurately fit real time-stamp data from Reddit and Twitter. We also show that RSC can be used to spot outliers and detect users with non-human behavior, such as bots. We validate RSC using real data consisting of over 35 million postings from Twitter and Reddit. RSC consistently provides a better fit to real data and clearly outperform existing models for human dynamics. RSC was also able to detect bots with a precision higher than 94%.

Fast fractal stack: fractal analysis of computed tomography scans of the lung

This paper proposes a new feature extraction method: the Fast Fractal Stack, or FFS. The extraction algorithm consists in decomposing the input grayscale image into a stack of binary images from which the fractal dimension values are computed, resulting in a compact and highly descriptive set of features. We evaluated FFS for the task of classification of interstitial lung diseases in computed tomography (CT) scans, applied on a database of 248 CT images from 67 patients. The proposed approach performs well, improving the classification accuracy when compared to other feature extraction algorithms. Additionally, the FFS extraction algorithm is efficient, with a computational cost linear with respect to input image size.

Vote-and-Comment: Modeling the Coevolution of User Interactions in Social Voting Web Sites

In social voting Web sites, how do the user actions – up-votes, down-votes and comments – evolve over time? Are there relationships between votes and comments? What is normal and what is suspicious? These are the questions we focus on. We analyzed over 20,000 submissions corresponding to more than 100 million user interactions from three social voting Web sites: Reddit, Imgur and Digg. Our first contribution is two discoveries: (i) the number of comments grows as a power-law on the number of votes and (ii) the time between a submission creation and a users reaction obeys a log-logistic distribution. Based on these patterns, we propose VnC (Vote-and-Comment), a parsimonious but accurate and scalable model that models the coevolution of user activities. In our experiments on real data, VnC outperformed state-of-the-art baselines on accuracy. Additionally, we illustrate VnC usefulness for forecasting and outlier detection.

Unveiling smoke in social images with the SmokeBlock approach

Can we use information from social media and crowdsourced images to detect smoke and assist rescue forces? While there are computer vision methods for detecting smoke, they require movement information extracted from video data. In this paper we propose SmokeBlock: a method that is able to segment and detect smoke in still images. SmokeBlock uses superpixel segmentation and extracts local color and texture features from images to spot smoke. We used real data from Flickr and compared SmokeBlock against state-of-the-art methods for feature extraction. Our method achieved performance superior than the competitors, for the task of smoke detection. Our findings shall support further investigations in the field of image analysis, in particular, concerning images captured with mobile devices.

Modeling Temporal Activity to Detect Anomalous Behavior in Social Media

Social media has become a popular and important tool for human communication. However, due to this popularity, spam and the distribution of malicious content by computer-controlled users, known as bots, has become a widespread problem. At the same time, when users use social media, they generate valuable data that can be used to understand the patterns of human communication. In this article, we focus on the following important question: Can we identify and use patterns of human communication to decide whether a human or a bot controls a user? The first contribution of this article is showing that the distribution of inter-arrival times (IATs) between postings is characterized by following four patterns: (i) heavy-tails, (ii) periodic-spikes, (iii) correlation between consecutive values, and (iv) bimodallity. As our second contribution, we propose a mathematical model named Act-M (Activity Model). We show that Act-M can accurately fit the distribution of IATs from social media users. Finally, we use Act-M to develop a method that detects if users are bots based only on the timing of their postings. We validate Act-M using data from over 55 million postings from four social media services: Reddit, Twitter, Stack-Overflow, and Hacker-News. Our experiments show that Act-M provides a more accurate fit to the data than existing models for human dynamics. Additionally, when detecting bots, Act-M provided a precision higher than 93% and 77% with a sensitivity of 70% for the Twitter and Reddit datasets, respectively.

BREATH: Heat Maps Assisting the Detection of Abnormal Lung Regions in CT Scans

Computed Tomography (CT) scans are often employed to diagnose lung diseases, as abnormal tissue regions may indicate whether proper treatment is required. However, detecting specific regions containing abnormalities in a CT scan demands time and effort of specialists. Moreover, different parts of a single lung image may present both normal and abnormal characteristics, what makes inaccurate the classification of a single lung as healthy (normal) or not. In this paper we propose the BREATH method, capable of detecting abnormalities in lung tissue regions, highlighting them by means of a heat map visualization. The method starts by segmenting lung tissues using a superpixel-based approach, followed by the training of a statistical model to represent normal tissues and, finally, the generation of a heat map showing abnormal regions that require attention from the physicians. We validated our statistical model using a dataset with 246 lung CT scans, where 40 are healthy and the remaining present varying diseases. Experimental results show that BREATH is accurate for lung segmentation with F-Measure of up to 0.99. The statistical modeling of healthy and abnormal lung regions has shown almost no overlap, and the detection of superpixels containing abnormalities presented precision values higher than 86%, for all values of recall. These values support our claim that the heat map representation of BREATH for the abnormal detection can be used as an intuitive method to assist physicians during the diagnosis.

Fire Detection from Social Media Images by Means of Instance-Based Learning

Social media can provide valuable information to support decision making in crisis management, such as in accidents, explosions, and fires. However, much of the data from social media are images, which are uploaded at a rate that makes it impossible for human beings to analyze them. To cope with that problem, we design and implement a database-driven architecture for fast and accurate fire detection named FFireDt. The design of FFireDt uses the instance-based learning through indexed similarity queries expressed as an extension of the relational Structured Query Language. Our contributions are: (i) the design of the Fast-Fire Detection (\(FFireDt\)), which achieves efficiency and efficacy rates that rival to the state-of-the-art techniques; (ii) the sound evaluation of 36 image descriptors, for the task of image classification in social media; (iii) the evaluation of content-based indexing with respect to the construction of instance-based classification systems; and (iv) the curation of a ground-truth annotated dataset of fire images from social media. Using real data from Flickr, the experiments showed that system \(FFireDt\) was able to achieve a precision for fire detection comparable to that of human annotators. Our results are promising for the engineering of systems to monitor images uploaded to social media services.

MFS-Map: efficient context and content combination to annotate images

Automatic image annotation provides textual description to images based on content and context information. Since images may present large variability, image annotation methods often employ multiple extractors to represent visual content considering local and global features under different visual aspects. As result, an important aspect of image annotation is the combination of context and content representations. This paper proposes MFS-Map (Multi-Feature Space Map), a novel image annotation method that manages the problem of combining multiple content and context representations when annotating images. The advantage of MFS-Map is that it does not represent visual and textual features by a single large feature vector. Rather, MFS-Map divides the problem into feature subspaces. This approach allows MFS-Map to improve its accuracy by identifying the features relevant for each annotation. We evaluated MFS-Map using two publicly available datasets: MIR Flickr and Image CLEF 2011. MFS-Map obtained both superior precision and faster speed when compared to other widely employed annotation methods.