Obtaining and representing common-sense knowledge, useful in a robotics scenario for planning and making inference about the robots’ surroundings, is a challenging problem, because such knowledge is typically found in unstructured repositories such as text corpora or small handmade resources. The work described in this paper presents a methodology for automatically creating a default knowledge ...

The Multiple Instance Learning (MIL) framework has been extensively used to solve weakly labeled visual classification problems, where each image or video is treated as a bag of instances. Instance Space based MIL algorithms construct a classifier by modifying standard classifiers by defining the probability that a bag is of the target class as the maximum over the probabilities that its instan...

Instance segmentation is a challenging task aiming at classifying and segmenting all object instances of specific classes. While two-stage box-based methods achieve top performances in the image domain, they cannot easily extend their superiority into video domain. This because usually deal with features or images cropped from detected bounding boxes without alignment, failing to capture pixel-...

Document clustering is an unsupervised approach in which a large collection of documents (corpus) is subdivided into smaller, meaningful, identifiable, and verifiable sub-groups (clusters). Meaningful representation of documents and implicitly identifying the patterns, on which this separation is performed, is the challenging part of document clustering. We have proposed a document clustering t...

estimating similarity is expressed in many domains and sciences. for instance, data mining, web mining, clustering, search engines, ontology mapping and social networks require the definition and deployment of similarity. user similarity in social networks is one of the main problems and has many applications in this area. in this paper, a new method is introduced for combining structural and n...

Clustering is a basic tool in unsupervised machine learning and data mining. Distance-based clustering algorithms rarely have the means to autonomously come up with the correct number of clusters from the data. A recent approach to identifying the natural clusters is to compare the point densities in different parts of the sample space. In this paper we put forward an agglomerative clustering a...

We examined the effectiveness of post-retrieval clustering that was based on the visual similarities among images to enhance the instance recall in the photo retrieval task of ImageCLEF 2008. The visual similarities are defined by the example visual concepts that were provided for the automatic photo indexing task. We tested two types of visual concepts and two kinds of clustering methods, hier...

Semi-supervised clustering approaches have emerged as an option for enhancing clustering results. These algorithms use external information to guide the clustering process. In particular, semi-supervised hierarchical clustering approaches have been explored in many fields in the last years. These algorithms provide efficient and personalized hierarchical overviews of datasets. To the best of th...

This work proposed a novel learning objective to train a deep neural network to perform end-to-end image pixel clustering. We applied the approach to instance segmentation, which is at the intersection of image semantic segmentation and object detection. We utilize the most fundamental property of instance labeling – the pairwise relationship between pixels – as the supervision to formulate the...