Lucas Pascotti Valem

Lucas Pascotti Valem
São Paulo State University | Unesp · Departamento de Estatistica, Matemática Aplicada e Computação DEMAC

PhD Student in Computer Science

About

33
Publications
1,321
Reads
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113
Citations
Citations since 2016
32 Research Items
111 Citations
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Publications

Publications (33)
Article
Image classification is a critical topic due to its wide application and several challenges associated. Despite the huge progress made last decades, there is still a demand for context-aware image representation approaches capable of taking into the dataset manifold for improving classification accuracy. In this work, a representation learning appr...
Article
Significant challenges still remain despite the impressive recent advances in machine learning techniques, particularly in multimedia data understanding. One of the main challenges in real-world scenarios is the nature and relation between training and test datasets. Very often, only small sets of coarse-grained labeled data are available to train...
Article
Full-text available
Person Re-ID has been gaining a lot of attention and nowadays is of fundamental importance in many camera surveillance applications. The task consists of identifying individuals across multiple cameras that have no overlapping views. Most of the approaches require labeled data, which is not always available, given the huge amount of demanded data a...
Article
Due to the possibility of capturing complex relationships existing between nodes, many applications benefit from being modeled with graphs. However, performance issues can be observed in large-scale networks, making it computationally unfeasible to process in various scenarios. Graph Embedding methods emerge as a promising solution for finding low-...
Preprint
Full-text available
In general, biometry-based control systems may not rely on individual expected behavior or cooperation to operate appropriately. Instead, such systems should be aware of malicious procedures for unauthorized access attempts. Some works available in the literature suggest addressing the problem through gait recognition approaches. Such methods aim a...
Article
Full-text available
In general, biometry-based control systems may not rely on individual expected behavior or cooperation to operate appropriately. Instead, such systems should be aware of malicious procedures for unauthorized access attempts. Some works available in the literature suggest addressing the problem through gait recognition approaches. Such methods aim a...
Article
Full-text available
Visual features and representation learning strategies experienced huge advances in the previous decade, mainly supported by deep learning approaches. However, retrieval tasks are still performed mainly based on traditional pairwise dissimilarity measures, while the learned representations lie on high dimensional manifolds. With the aim of going be...
Article
Contextual information, defined in terms of the proximity of feature vectors in a feature space, has been successfully used in the construction of search services. These search systems aim to exploit such information to effectively improve ranking results, by taking into account the manifold distribution of features usually encoded. In this paper,...
Conference Paper
The CBIR (Content-Based Image Retrieval) systems are one of the main solutions for image retrieval tasks. These systems are mainly supported by the use of different visual features and machine learning methods. As distinct features produce complementary ranking results with different effectiveness performance, a promising solution consists in combi...
Article
Nowadays, there is a great variety of visual features available for image retrieval tasks. While fusion strategies have been established as a promising alternative, an inherent difficulty in unsupervised scenarios is the task of selecting the features to combine. In this paper, a Graph-based Selective Rank Fusion is proposed. The graph is used to r...
Article
Despite the continuous advances in image retrieval technologies, performing effective and efficient content‐based searches remains a challenging task. Unsupervised iterative re‐ranking algorithms have emerged as a promising solution and have been widely used to improve the effectiveness of multimedia retrieval systems. Although substantially more e...
Conference Paper
Mainly due to the evolution of technologies to store and share images, the growth of image collections have been remarkable for years. Therefore, developing effective methods to index and retrieve such extensive available visual information is indispensable. The CBIR (Content-Based Image Retrieval) systems are one of the main solutions for image re...
Article
Several visual features have been developed for content-based image retrieval in the last decades, including global, local and deep learning-based approaches. However, despite the huge advances in features development and mid-level representations, a single visual descriptor is often insufficient to achieve effective retrieval results in several sc...
Chapter
Estimating the effectiveness of retrieval systems in unsupervised scenarios consists in a task of crucial relevance. By exploiting estimations which dot not require supervision, the retrieval results of many applications as rank aggregation and relevance feedback can be improved. In this paper, a novel approach for unsupervised effectiveness estima...
Article
Full-text available
Accurately ranking images and multimedia objects is of paramout relevance in many retrieval and learning tasks. Manifold learning methods have been investigated for ranking mainly due to its capacity of taking into account the intrinsic global manifold structure. In this paper, a novel manifold ranking algorithm is proposed based on hypergraphs for...
Conference Paper
Despite the major advances on feature development for low and mid-level representations, a single visual feature is often insufficient to achieve effective retrieval results in different scenarios. Since diverse visual properties provide distinct and often complementary information for a same query, the combination of different features, including...
Article
The increasing amount of multimedia data collections available today evinces the pressing need for methods capable of indexing and retrieving this content. Despite the continuous advances in multimedia features and representation models, to establish an effective measure for comparing different multimedia objects still remains a challenging task. W...
Article
The Unsupervised Distance Learning Framework (UDLF) [1] is a software developed to facilitate the general use and evaluation of novel unsupervised learning methods. These methods aim at post-processing the ranking information for different tasks, being especially useful for multimedia retrieval. The major advantage of UDLF is that it provides a uni...
Conference Paper
Full-text available
Often, different segments of a video may be more or less attractive for people depending on their experience in watching it. Due to this subjectiveness, the challenging task of automatically predicting whether a video segment is interesting or not has attracted a lot of attention. Current solutions are usually based on learning models trained with...
Article
Despite the consistent advances in visual features and other Multimedia Information Retrieval (MIR) techniques, measuring the similarity among multimedia objects is still a challenging task for an effective retrieval. In this scenario, similarity learning approaches capable of improving the effectiveness of retrieval in an unsupervised way are indi...
Conference Paper
Full-text available
The evolution of technologies to store and share images has made imperative the need for methods to index and retrieve multimedia information based on visual content. The CBIR (Content-Based Image Retrieval) systems are the main solution in this scenario. Originally, these systems were solely based on the use of low-level visual features, but evolv...
Conference Paper
Full-text available
Due to the increasing availability of image and multimedia collections, unsupervised post-processing methods, which are capable of improving the effectiveness of retrieval results without the need of user intervention, have become indispensable. This paper presents the Unsupervised Distance Learning Framework (UDLF), a software which enables an eas...
Conference Paper
Full-text available
Despite the consistent advances in visual features and other Content-Based Image Retrieval techniques, measuring the similarity among images is still a challenging task for effective image retrieval. In this scenario, similarity learning approaches capable of improving the effectiveness of retrieval in an unsupervised way are indispensable. A novel...
Conference Paper
Full-text available
Various unsupervised learning methods have been proposed with significant improvements in the effectiveness of image search systems. However, despite the relevant effectiveness gains, these approaches commonly require high computation efforts, not addressing properly efficiency and scalability requirements. In this paper, we present a novel unsuper...

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