Spectral clustering is a popular and effective clustering method but known to face two significant challenges: scalability and out-of-sample extension. In this paper, we extend the work of Chen (ICPR 2018) on the speed scalability of spectral clustering in the setting of cosine similarity to deal with massive or online data that are too large to be fully loaded into computer memory. We start by assuming a small batch of data drawn from the full set and develop an efficient procedure that learns both the nonlinear embedding and clustering map from the sample and extends them easily to the rest of the data as they are gradually loaded. We then introduce an automatic approach to selecting the optimal value of the sample size. The combination of the two steps leads to a streamlined memory-efficient algorithm that only uses a small number of batches of data (as they become available), with memory and computational costs that are independent of the size of the data. Experiments are conducted on benchmark data to demonstrate the fast speed and excellent accuracy of the proposed algorithm. We conclude the paper by pointing out several future research directions.

Bees play an important role as pollinating agents, contributing to the reproduction of many plant species around the world. Brazil is the home for different species of stingless bees, with around 200 registered species out of the more than 500 species classified worldwide. Each species constructs the entrance to its colony in an unique but similar way among colonies of the same species. In this work, we proposed a new dataset created in collaboration with stingless beekeepers from Brazil for the exploration of stingless bee species classification. The dataset consists of 158 samples distributed unequally among the 13 species: Boca de Sapo, Borá, Bugia, Iraí, Japurá, Jataí, Lambe Olhos, Mandaguari, Mirim Droryana, Mirim Preguiça, Moça Branca, Mandaçaia, and Tubuna. The results presented in this work were obtained using deep learning models (i.e. CNN architectures) such as VGG and DenseNet, which are commonly used for image classification task in different application domains. Pre-trained models from ImageNet were used, along with transfer learning techniques, and due to the small size of the dataset, data augmentation techniques were applied, resulting in an expanded dataset of 1,106 samples. The experimental results demonstrated that the DenseNet model achieved the best results, reaching an accuracy of 95%. The dataset created will be also made available as a contribution of these work. As far as we know, the stingless bee species identification task based on the colony entrance is addressed for the first time in this work.

Breast tumor is one of the most prominent indicators for diagnosis of breast cancer. Magnetic Resonance Imaging (MRI) is a relevant imaging modality tool for breast cancer screening. Moreover, an accurate 3D segmentation of breast tumors from MRI scans plays a key role in the analysis of the disease. This paper presents a pipeline to automatically segment multiple tumors in breast MRI scans, following the

methodology proposed by one previous study, addressing its limitations in detecting multiple tumors and automatically selecting seed points using a 3D region growing algorithm. The pre-processing includes bias field correction, data normalization, and image filtering. The segmentation process involved several steps, including identifying high-intensity points, followed by identifying high-intensity regions using k-means clustering.

Then, the centers of the regions were used as seeds for the 3D region growing algorithm, resulting in a mask with 3D structures. These masks were then analyzed in terms of their volume, compactness, and circularity.

Despite the need for further adjustments in the model parameters, the successful segmentation of four tumors proved that our solution is a promising approach for automatic multi-tumor segmentation with the potential to be combined with a classification model relying on the characteristics of the segmented structures.