Mirela T. Cazzolato

A Label-Scaled Similarity Measure for Content-Based Image Retrieval

Content-Based Image Retrieval (CBIR) has proven to be a suitable complement to traditional text-based searching. CBIR applications rely on two main steps, namely the representation of the images, and the similarity measuring between two represented images. Although modern segmentation and learning algorithms enable the accurate representation of local and global features within an image, how to properly compare the segmented objects is still an open issue. In this study, we propose a new comparison method called Counting-Labels Similarity Measure (CL-Measure). Our approach calculates the similarity between two images by comparing the labeled regions within these images and by balancing the influence of each label according to its predominance in both non-metric and metric fashion. The experiments on a real dataset of dermatological ulcers show that CL-Measure achieves a higher Precision for all values of Recall compared to its competitors in retrieval tasks.

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.

Efficiently Indexing Multiple Repositories of Medical Image Databases

Performing content-based image retrieval over large repositories of medical images demands efficient computational techniques. The use of such techniques is intended to speed up the work of physicians, who often have to deal with information from multiple data repositories. When dealing with multiple data repositories, the common computational approach is to search each repository separately and merge the multiple results into one final response, which slows down the whole process. This can be improved if we build a mechanism able to search several repositories as if they were a single one, i.e. a mechanism to search the whole domain of medical images. Aiming at this goal, we propose the Domain Index, a new category of index structures aimed at efficiently searching domains of data, regardless of the repository to which they belong. To evaluate our proposal, we carried out experiments over multiple mammography repositories involving k Nearest Neighbor (kNN) and Range queries. The results show that images from any repository are seamlessly retrieved, even sustaining gains in performance of up to 36% in kNN queries and up to 7% in Range queries. The experimental evaluation shows that the Domain Index allows fast retrieval from multiple data repositories for medical systems, allowing a better performance in similarity queries over them.

Efficient and Reliable Estimation of Cell Positions

Sequences of microscopic images feature the dynamics of developing embryos. Automatically tracking the cells from such sequences of images allows understanding the dynamics which a living element demands to know its cells movement, which ideally should take place in real-time. The traditional tracking pipeline starts with image acquisition, data transfer, image segmentation to separate cells from the background, and then the actual tracking step. To speed up this pipeline, we hypothesize that a process capable of predicting the cell motion according to previous observations is useful. The solution must be accurate, fast and lightweight, and be able to iterate between the various components. In this work we propose CM-Predictor, which takes advantage of previous positions of cells to estimate their motion. When estimation takes place, we can omit costly acquisition, transfer and process of images, speeding up the tracking pipeline. The designed solution monitors the error of prediction, adapting the model whenever needed. For validation, we use four different datasets with sequences of images with developing embryos. Then we compare the estimated motion vectors of CM-Predictor with traditional tracking methods. Experimental results show that CM-Predictor is able to accurately estimate the motion vectors. In fact, CM-Predictor maintains the prediction quality of other algorithms and performs faster than them.

Semantic Similarity Group By Operators for Metric Data

Grouping operators summarize data in DBMS arranging elements in groups using identity comparisons. However, for metric data, grouping by identity is seldom useful, since adopting the concept of similarity is often a better fit. There are operators that can group data elements using similarity. However, the existing operators do not achieve good results for certain data domains or distributions. The major contributions of this work are a novel operator called the SGB-Vote that assign groups using an election involving already assigned groups and an extension for current operators bounds each group to a maximum amount of the nearest neighbors. The operators were implemented in a framework and evaluated using real and synthetic datasets from diverse domains considering both quality of and execution time. The results obtained show that the proposed operators produce higher quality groups in all tested datasets and highlight that the operators can efficiently run inside a DBMS.

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.

ClusMAM: fast and effective unsupervised clustering of large complex datasets using metric access methods

An efficient and effective clustering process is a core task of data mining analysis, and has become more important in the nowadays scenario of big data, where scalability is an issue. In this paper we present the ClusMAM method, which proposes a new strategy for clustering large complex datasets through metric access methods. ClusMAM aims at accelerating the process of relational partitional clustering by taking advantage of the inherent node separations of metric access methods. In comparison with other methods from the literature, ClusMAM is up to four orders of magnitude faster than the competitors maintaining clustering quality. Additionally, ClusMAM exploits the datasets to find compact and coherent clusters, suggesting the number of clusters k found in the data. The method was evaluated employing synthetic and real datasets, and the behavior of the method was consistent regarding the number of distance calculations and time required for the clustering process as well.

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.