Assessment of clustering techniques to support the analyses of soybean seed vigor.

Soy is the main product of Brazilian agriculture and the fourth most cultivated bean globally. Since soy cultivation tends to increase and due to this large market, the guarantee of product quality is an indispensable factor for enterprises to stay competitive. Industries perform vigor tests to acquire information and evaluate the quality of soy planting. The tetrazolium test, for example, provides information about moisture damage, bedbugs, or mechanical damage. However, the verification of the damage reason and its severity are done by an analyst, one by one. Since this is massive and exhausting work, it is susceptible to mistakes. Proposals involving different supervised learning approaches, including active learning strategies, have already been used, and have brought significant results. Therefore, this paper analyzes the performance of non-supervised techniques for classifying soybeans. An extensive experimental evaluation was performed, considering (9) different clustering algorithms (partitional, hierarchical, and density-based) applied to 5 image datasets of soybean seeds submitted to the tetrazolium test, including different damages and/or their levels. To describe those images, we considered 18 extractors of traditional features. We also considered four metrics (accuracy, FOWLKES, DAVIES, and CALINSKI) and two-dimensionality reduction techniques (principal component analysis and t-distributed stochastic neighbor embedding) for validation. Results show that this paper presents essential contributions since it makes it possible to identify descriptors and clustering algorithms that shall be used as preprocessing in other learning processes, accelerating and improving the classification process of key agricultural problems.

Classification of Skin Lesion through Active Learning Strategies.

BACKGROUND AND OBJECTIVE: According to the National Cancer Institute, among all malignant tumors, non-melanoma skin cancer, and melanoma are the most frequent in Brazil. Despite having a lower incidence, the melanoma type has accelerated growth and greater lethality. Several studies have been performed in recent years in the computer vision area to assist in the early diagnosis of skin cancer. Despite being widely used and presenting good results, deep learning approaches require a large amount of annotated data and considerable computational cost for training the model. Therefore, the present work explores active learning approaches to select a small set of more informative data for training the classifier. For that, different selection criteria are considered to obtain more effective and efficient classifiers for skin lesions. METHODS: We perform an extensive experimental evaluation considering three datasets and different learning strategies and scenarios for validation. In addition to data augmentation, we evaluated two segmentation strategies considering the U-net CNN model and the Fully Convolutional Networks (FCN) with a manual expert review. We also analyzed the best (handcrafted and deep) features that describe each skin lesion and the most suitable classifiers and combinations (extractor-classifier) for this context. The active learning approach evaluated different criteria based on uncertainty, diversity, and representativeness to select the most informative samples. The strategies used were Decreasing Boundary Edges, Entropy, Least Confidence, Margin Sampling, Minimum-Spanning Tree Boundary Edges, and Root-Distance based Sampling. RESULTS: It can be observed that the segmentation with FCN and manual correction by the specialist, the Border-Interior Classification (BIC) extractor, and the Random Forest (RF) classifier showed a better performance. Regarding the active learning approach, the Margin Sampling strategy presented the best classification accuracies (about 93%) with only 35% of the training set compared to the traditional learning approach (which requires the entire set). CONCLUSIONS: According to the results, it is possible to observe that the selection strategies allow for achieving high accuracies faster (fewer learning iterations) and with a smaller amount of labeled samples compared to the traditional learning approach. Hence, active learning can contribute significantly to the diagnosis of skin lesions, beneficially reducing specialists' annotation costs.

Active semi-supervised learning for biological data classification.

Due to datasets have continuously grown, efforts have been performed in the attempt to solve the problem related to the large amount of unlabeled data in disproportion to the scarcity of labeled data. Another important issue is related to the trade-off between the difficulty in obtaining annotations provided by a specialist and the need for a significant amount of annotated data to obtain a robust classifier. In this context, active learning techniques jointly with semi-supervised learning are interesting. A smaller number of more informative samples previously selected (by the active learning strategy) and labeled by a specialist can propagate the labels to a set of unlabeled data (through the semi-supervised one). However, most of the literature works neglect the need for interactive response times that can be required by certain real applications. We propose a more effective and efficient active semi-supervised learning framework, including a new active learning method. An extensive experimental evaluation was performed in the biological context (using the ALL-AML, Escherichia coli and PlantLeaves II datasets), comparing our proposals with state-of-the-art literature works and different supervised (SVM, RF, OPF) and semi-supervised (YATSI-SVM, YATSI-RF and YATSI-OPF) classifiers. From the obtained results, we can observe the benefits of our framework, which allows the classifier to achieve higher accuracies more quickly with a reduced number of annotated samples. Moreover, the selection criterion adopted by our active learning method, based on diversity and uncertainty, enables the prioritization of the most informative boundary samples for the learning process. We obtained a gain of up to 20% against other learning techniques. The active semi-supervised learning approaches presented a better trade-off (accuracies and competitive and viable computational times) when compared with the active supervised learning ones.

Contributing to agriculture by using soybean seed data from the tetrazolium test.

Agribusiness has a great relevance in the world׳s economy. It generates a considerable impact in the gross national product of several nations. Hence, it is the major driver of many national economies. Nowadays, from each new planting to harvesting process it is mandatory and crucial to apply some kind of technology to optimize a given singular process, or even the entire cropping chain. For instance, digital image analysis joined with machine learning methods can be applied to obtain and guarantee a higher quality of the harvest, leading to not only a greater profit for producers, but also better products with lower cost to the final consumers. Thus, to provide this possibility this work describes a visual feature dataset from soybean seed images obtained from the tetrazolium test. This is a test capable to define how healthy a given seed is (e.g. how much the plant will produce, or if it is resistant to inclement weather, among others). To answer these questions we proposed this dataset which is the cornerstone to provide an effective classification of the soybean seed vigor (i.e. an extremely tiresome human visual inspection process). Besides, as one of the most prominent international commodity, the soybean production must follow rigid quality control process to be part of world trade. Hence, small mistakes in the seed vigor definition of a given seed lot can lead to huge losses.

PRoSPer: perceptual similarity queries in medical CBIR systems through user profiles.

In this paper, we present a novel approach to perform similarity queries over medical images, maintaining the semantics of a given query posted by the user. Content-based image retrieval systems relying on relevance feedback techniques usually request the users to label relevant/irrelevant images. Thus, we present a highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather the user perceptual similarity. The profiles maintain the settings desired for each user, allowing tuning of the similarity assessment, which encompasses the dynamic change of the distance function employed through an interactive process. Experiments on medical images show that the method is effective and can improve the decision making process during analysis.