Browsing by Author "Lacerda, Anisio Mendes"

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    Automatic and online setting of similarity thresholds in content-based visual information retrieval problems.
    (2016) Bessas, Izaquiel L.; Pádua, Flávio Luis Cardeal; Assis, Guilherme Tavares de; Cardoso, Rodrigo T. N.; Lacerda, Anisio Mendes
    Several information recovery systems use functions to determine similarity among objects in a collection. Such functions require a similarity threshold, from which it becomes possible to decide on the similarity between two given objects. Thus, depending on its value, the results returned by systems in a search may be satisfactory or not. However, the definition of similarity thresholds is difficult because it depends on several factors. Typically, specialists fix a threshold value for a given system, which is used in all searches. However, an expert-defined value is quite costly and not always possible. Therefore, this study proposes an approach for automatic and online estimation of the similarity threshold value, to be specifically used by content-based visual information retrieval system (image and video) search engines. The experimental results obtained with the proposed approach prove rather promising. For example, for one of the case studies, the performance of the proposed approach achieved 99.5 % efficiency in comparison with that obtained by a specialist using an empirical similarity threshold. Moreover, such automated approach becomes more scalable and less costly.
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    Evaluation of interest point matching methods for projective reconstruction of 3d scenes.
    (2016) Brito, Darlan Nunes de; Nunes, Cristiano Fraga Guimarães; Pádua, Flávio Luis Cardeal; Lacerda, Anisio Mendes
    This work evaluates the application of different state-of-the-art methods for interest point matching, aiming the robust and efficient projective reconstruction of three-dimensional scenes. Projective reconstruction refers to the computation of the structure of a scene from images taken with uncalibrated cameras. To achieve this goal, it is essential the usage of an effective point matching algorithm. Even though several point matching methods have been proposed in the literature, their impacts in the projective reconstruction task have not yet been carefully studied. Our evaluation uses as criterion the estimated epipolar, reprojection and reconstruction errors, as well as the running times of the algorithms. Specifically, we compare five different techniques: SIFT, SURF, ORB, BRISK and FREAK. Our experiments show that binary algorithms such as, ORB and BRISK, are so accurate as float point algorithms like SIFT and SURF, nevertheless, with smaller computational cost.
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    Individualized extreme dominance (IndED) : a new preference- based method for multi-objective recommender systems.
    (2021) Fortes, Reinaldo Silva; Sousa, Daniel Xavier de; Coelho, Dayanne Gouveia; Lacerda, Anisio Mendes; Gonçalves, Marcos André
    Recommender Systems (RSs) make personalized suggestions of relevant items to users. However, the concept of relevance may involve different quality aspects (objectives), such as accuracy, novelty, and diversity. In addition, users may have their own expectations regarding what characterizes a good recommendation. More specifically, individual users may wish to prioritize the multiple objectives in different proportions based on their preferences. Previous studies on Multi-Objective (MO) recommendation do not prioritize objectives according to the individual users’ preferences systematically or are biased towards a single objective as in re-ranking strategies. Moreover, traditional preference-based multi-objective solutions do not address the specificities of RSs. In this work, we pro- pose IndED (Individualized Extreme Dominance), a new preference-based method for MORSs. IndED explores the concepts of Extreme Dominance and Statistical Significance Tests in order to define a new Pareto-based dominance relation that guides the optimization search considering users’ preferences. We also consider a new decision making process that minimizes the distance to the individual user’s preferences. Experiments show that IndED outperformed competitive baselines, obtaining results closer to the users’ preferences and better balancing the objectives trade-offs. IndED is also the method that obtains the best performance regarding the most difficult objective in each considered scenario.