Ricardo da S. Torres

article

A unified model for accelerating unsupervised iterative re‐ranking algorithms

Flávia Pisani, Lucas Pascotti Valem, Daniel Carlos Guimarães Pedronette, Ricardo da S. Torres, more

Concurrency and Computation: Practice and Experience > 32 > 14 > n/a - n/a

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 efficient than related approaches based on diffusion...

article

Multivariate cyclical data visualization using radial visual rhythms: A case study in phenology analysis

Greice C. Mariano, Vanessa G. Staggemeier, Leonor Patricia Cerdeira Morellato, Ricardo da S. Torres

Ecological Informatics > 2018 > 46 > C > 19-35

Phenology is a traditional science that investigates the periodic phenomena of plants and animals and their relations to environmental conditions. Typically plant phenological studies are based on observations made by phenology experts in the field over time and the correlation with climate data collected by weather sensors. Although within the visualization community several approaches have been...

article

Graph-based bag-of-words for classification

Fernanda B. Silva, Rafael de O. Werneck, Siome Goldenstein, Salvatore Tabbone, more

Pattern Recognition > 2018 > 74 > C > 266-285

This paper introduces the Bag of Graphs (BoG), a Bag-of-Words model that encodes in graphs the local structures of a digital object. We present a formal definition, introducing concepts and rules that make this model flexible and adaptable for different applications. We define two BoG-based methods – Bag of Singleton Graphs (BoSG) and Bag of Visual Graphs (BoVG), which create vector representations...

article

Contextual Spaces Re‐Ranking: accelerating the Re‐sort Ranked Lists step on heterogeneous systems

Flávia Pisani, Daniel C. G. Pedronette, Ricardo da S. Torres, Edson Borin

Concurrency and Computation: Practice and Experience > 29 > 22 > n/a - n/a

Re‐ranking algorithms have been proposed to improve the effectiveness of content‐based image retrieval systems by exploiting contextual information encoded in distance measures and ranked lists. In this paper, we show how we improved the efficiency of one of these algorithms, called Contextual Spaces Re‐Ranking (CSRR). One of our approaches consists in parallelizing the algorithm with OpenCL to use...

article

Unsupervised rank diffusion for content-based image retrieval

Daniel Carlos Guimarães Pedronette, Ricardo da S. Torres

Neurocomputing > 2017 > 260 > C > 478-489

Despite the continuous development of features and mid-level representations, effectively and reliably measuring the similarity among images remains a challenging problem in image retrieval tasks. Once traditional measures consider only pairwise analysis, context-sensitive measures capable of exploiting the intrinsic manifold structure became indispensable for improving the retrieval performance....

article

Remote Sensing Image Classification Using Genetic-Programming-Based Time Series Similarity Functions

Alexandre E. Almeida, Ricardo da S. Torres

IEEE Geoscience and Remote Sensing Letters > 2017 > 14 > 9 > 1499 - 1503

In several applications, the automatic identification of regions of interest in remote sensing images is based on the assessment of the similarity of associated time series, i.e., two regions are considered as belonging to the same class if the patterns found in their spectral information observed over time are somewhat similar. In this letter, we investigate the use of a genetic programming (GP)...

chapter

Fusion of genetic-programming-based indices in hyperspectral image classification tasks

Juan F. Hernandez Albarracin, Edemir Ferreira, Jefersson A. dos Santos, Ricardo da S. Torres

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) > 554 - 557

IGARSS 2017 - 2017 IEEE International Geoscience and Remote Sensing Symposium

This paper introduces a two-step hyper- and multi-spectral image classification approach. The first step relies on the use of a genetic programming (GP) framework to both select and combine appropriate bands. The second step is concerned with the image classification itself. We present two strategies for multi-class classification problems based on the combination of GP-based indices defined in binary...

chapter

Semantic segmentation of vegetation images acquired by unmanned aerial vehicles using an ensemble of ConvNets

Keiller Nogueira, Jefersson A. dos Santos, Leonardo Cancian, Bruno D. Borges, more

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) > 3787 - 3790

IGARSS 2017 - 2017 IEEE International Geoscience and Remote Sensing Symposium

Vegetation segmentation in high resolution images acquired by unmanned aerial vehicles (UAVs) is a challenging task that requires methods capable of learning high-level features while dealing with fine-grained data. In this paper, we propose a combination of different methods of semantic segmentation based on Convolutional Networks (ConvNets) to obtain highly accurate segmentation of individuals of...

article

Introducing digital cameras to monitor plant phenology in the tropics: applications for conservation

Bruna Alberton, Ricardo da S. Torres, Leonardo F. Cancian, Bruno D. Borges, more

Perspectives in Ecology and Conservation > 2017 > 15 > 2 > 82-90

The application of digital cameras to monitor the environment is becoming global and changing the way of phenological data collection. The technique of repeated digital photographs to monitor plant phenology (phenocams) has increased due to its low-cost investment, reduced size, easy set up installation, and the possibility of handling high-resolution near-remote data. Considering the widespread use...

article

Nearest neighbors distance ratio open-set classifier

Pedro R. Mendes Júnior, Roberto M. Souza, Rafael De O. Werneck, Bernardo V. Stein, more

Machine Learning > 2017 > 106 > 3 > 359-386

In this paper, we propose a novel multiclass classifier for the open-set recognition scenario. This scenario is the one in which there are no a priori training samples for some classes that might appear during testing. Usually, many applications are inherently open set. Consequently, successful closed-set solutions in the literature are not always suitable for real-world recognition problems. The...

chapter

Towards vegetation species discrimination by using data-driven descriptors

Keiller Nogueira, Jefersson A. Dos Santos, Tamires Fornazari, Thiago Sanna Freire Silva, more

2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS) > 1 - 6

2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS)

Abstract—In this paper, we analyse the use of Convolutional Neural Networks (CNNs or ConvNets) to discriminate vegetation species with few labelled samples. To the best of our knowledge, this is the first work dedicated to the investigation of the use of deep features in such task. The experimental evaluation demonstrate that deep features significantly outperform wellknown feature extraction techniques...

article

Unsupervised Distance Learning for Plant Species Identification

Jurandy Almeida, Daniel C. G. Pedronette, Bruna C. Alberton, Leonor Patricia C. Morellato, more

IEEE Journal of Selected Topics in Applied Earth Observations and Remote... > 2016 > 9 > 12-1 > 5325 - 5338

Phenology is among the most trustworthy indicators of climate change effects on plants and animals. The recent application of repeated digital photographs to monitor vegetation phenology has provided accurate measures of plant life cycle changes over time. A fundamental requirement for phenology studies refers to the correct recognition of phenological patterns from plants by taking into account time...

article

PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps

Roger A. Leite, Lucas Mello Schnorr, Jurandy Almeida, Bruna Alberton, more

Information Sciences > 2016 > 372 > C > 181-195

article

Fusion of time series representations for plant recognition in phenology studies

Fabio A. Faria, Jurandy Almeida, Bruna Alberton, Leonor Patricia C. Morellato, more

Pattern Recognition Letters > 2016 > 83 > P2 > 205-214

Nowadays, global warming and its resulting environmental changes is a hot topic in different biology research area. Phenology is one effective way of tracking such environmental changes through the study of plant’s periodic events and their relationship to climate. One promising research direction in this area relies on the use of vegetation images to track phenology changes over time. In this scenario,...

article

TSS & TSB: Tensor scale descriptors within circular sectors for fast shape retrieval

Anderson M. Freitas, Ricardo da S. Torres, Paulo A.V. Miranda

Pattern Recognition Letters > 2016 > 83 > P3 > 303-311

We propose two novel region-based descriptors for shape-based image retrieval and analysis, which are built upon an extended tensor scale based on the Euclidean Distance Transform (EDT). First the tensor scale algorithm is applied to extract local structure thickness, orientation, and anisotropy as represented by the largest ellipse within a homogeneous region centered at each image pixel. In this...

article

A graph-based ranked-list model for unsupervised distance learning on shape retrieval

Daniel Carlos Guimarães Pedronette, Jurandy Almeida, Ricardo da S. Torres

Pattern Recognition Letters > 2016 > 83 > P3 > 357-367

Several re-ranking algorithms have been proposed recently. Some effective approaches are based on complex graph-based diffusion processes, which usually are time consuming and therefore inappropriate for real-world large scale shape collections. In this paper, we introduce a novel graph-based approach for iterative distance learning in shape retrieval tasks. The proposed method is based on the combination...

article

A correlation graph approach for unsupervised manifold learning in image retrieval tasks

Daniel Carlos Guimarães Pedronette, Ricardo da S. Torres

Neurocomputing > 2016 > 208 > C > 66-79

Effectively measuring the similarity among images is a challenging problem in image retrieval tasks due to the difficulty of considering the dataset manifold. This paper presents an unsupervised manifold learning algorithm that takes into account the intrinsic dataset geometry for defining a more effective distance among images. The dataset structure is modeled in terms of a Correlation Graph (CG)...

chapter

Learning to Combine Spectral Indices with Genetic Programming

Juan Felipe Hernandez Albarracin, Jefersson Alex dos Santos, Ricardo da S. Torres

2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) > 408 - 415

2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)

This paper introduces a Genetic Programming-based method for band selection and combination, aiming to support remote sensing image classification tasks. Relying on ground-truth data, our method selects spectral bands and finds the arithmetic combination of those bands (i.e., spectral index) that best separates examples of different classes. Experimental results demonstrate that the proposed method...

article

Phenological visual rhythms: Compact representations for fine-grained plant species identification

Jurandy Almeida, Jefersson A. dos Santos, Bruna Alberton, Leonor Patricia C. Morellato, more

Pattern Recognition Letters > 2016 > 81 > C > 90-100

Plant phenology, the study of recurrent life cycles events and its relationship to climate, is a key discipline in climate change research. In this context, digital cameras have been effectively used to monitor leaf flushing and senescence on vegetations across the world. A primary condition for the phenological observation refers to the correct identification of plants by taking into account time...

article

Time series-based classifier fusion for fine-grained plant species recognition

Fabio A. Faria, Jurandy Almeida, Bruna Alberton, Leonor Patricia C. Morellato, more

Pattern Recognition Letters > 2016 > 81 > C > 101-109

Global warming and its resulting environmental changes surely are ubiquitous subjects nowadays and undisputedly important research topics. One way of tracking such environmental changes is by means of phenology, which studies natural periodic events and their relationship to climate. Phenology is seen as the simplest and most reliable indicator of the effects of climate change on plants and animals...

INFONA - science communication portal

Search results for: Ricardo da S. Torres

A unified model for accelerating unsupervised iterative re‐ranking algorithms

Multivariate cyclical data visualization using radial visual rhythms: A case study in phenology analysis

Graph-based bag-of-words for classification

Contextual Spaces Re‐Ranking: accelerating the Re‐sort Ranked Lists step on heterogeneous systems

Unsupervised rank diffusion for content-based image retrieval

Remote Sensing Image Classification Using Genetic-Programming-Based Time Series Similarity Functions

Fusion of genetic-programming-based indices in hyperspectral image classification tasks

Semantic segmentation of vegetation images acquired by unmanned aerial vehicles using an ensemble of ConvNets

Introducing digital cameras to monitor plant phenology in the tropics: applications for conservation

Nearest neighbors distance ratio open-set classifier

Towards vegetation species discrimination by using data-driven descriptors

Unsupervised Distance Learning for Plant Species Identification

PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps

Fusion of time series representations for plant recognition in phenology studies

TSS & TSB: Tensor scale descriptors within circular sectors for fast shape retrieval

A graph-based ranked-list model for unsupervised distance learning on shape retrieval

A correlation graph approach for unsupervised manifold learning in image retrieval tasks

Learning to Combine Spectral Indices with Genetic Programming

Phenological visual rhythms: Compact representations for fine-grained plant species identification

Time series-based classifier fusion for fine-grained plant species recognition

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