Pathological Anatomy is moving toward computerizing processes mainly due to the extensive digitization of histology slides that resulted in the availability of many Whole Slide Images (WSIs). Their use is essential, especially in cancer diagnosis and research, and raises the pressing need for increasingly influential information archiving and retrieval systems. Picture Archiving and Communication Systems (PACSs) represent an actual possibility to archive and organize this growing amount of data. The design and implementation of a robust and accurate methodology for querying them in the pathology domain using a novel approach are mandatory. In particular, the Content-Based Image Retrieval (CBIR) methodology can be involved in the PACSs using a query-by-example task. In this context, one of many crucial points of CBIR concerns the representation of images as feature vectors, and the accuracy of retrieval mainly depends on feature extraction. Thus, our study explored different representations of WSI patches by features extracted from pre-trained Convolution Neural Networks (CNNs). In order to perform a helpful comparison, we evaluated features extracted from different layers of state-of-the-art CNNs using different dimensionality reduction techniques. Furthermore, we provided a qualitative analysis of obtained results. The evaluation showed encouraging results for our proposed framework. Graphical Abstract

The employment of personal robots or service robots has aroused much interest in recent years with an amazing growth of robotics in different domains. Although sophisticated humanoid robots have been developed, much more effort is needed for improving their cognitive capabilities. Interactions with humans and/or with other agents are still limited and not considered satisfactory. So, the way we store and represent knowledge in a cognitive architecture is fundamental in order to overcome these limitations and improve human-machine and machine-machine interactions. In this paper we propose an unsupervised approach for knowledge construction based on robot’s perception. Our approach makes use of Kohonen maps as unsupervised machine learning technique and allows the definition of semantic clusters from visual features perceived by the robot. Besides, a multimedia graph knowledge base using a pure formalism is presented, which can be actively used by personal robots in their classic activities like environment exploration or information gathering to represent and share the acquired knowledge, linking it to abstract concepts gifted with semantic relations.

The increasing of data volume together with heterogeneity of representations are basic challenges in several application and research contexts. This issue is stressed in the Semantic Web domain where huge of information have to be accessed by users and services in a common and simple way. In order to present different data in an integrated fashion we need of efficient and effective techniques to preserve information about them. In this paper we present a complete approach to integrate very large multimedia knowledge bases using well known formalisms for knowledge representation as ontologies. The proposed approach is supported by a software framework able to create a global ontology by the integration of existing ones automatizing the whole ontology building process. We present a case study on cultural heritage domain to show a real implementation of our approach.

The user is a basic component in the whole information management process and for this reason s/he has to be taken into account in the design and implementation of frameworks and systems for information retrieval. For this reason, the problem of defining efficient techniques for knowledge representation in a user's prospective is becoming a challenging topic in both academic and industrial community. Moreover, the large amount of available data induces several problems from different points of view and it is stressed in the bigdata vision. In this latter some approaches related to the semantic web could be used to improve the data models which underlie the implementation of user-based information retrieval applications. In this paper we propose the use of a semantic approach to design the structure of a multimedia BigData. In addition, the recognition of multimodal features to represent concepts and its attributes together with linguistic properties to relate them are an effective way to bridge the gap between the target semantic classes and the available low-level multimedia descriptors. Information about users (i.e. position) is also used to recognize his/her behaviour and improve the quality of the information retrieval process. Our framework has been implemented using a NoSQL graphdb populated from very large knowledge sources and mobile technologies. Extended experiments are presented to show the effectiveness of our approach.