Analysis and Re-use of Videos in Educational Digital Libraries with Automatic Scene Detection

The advent of modern approaches to education, like Massive Open Online Courses (MOOC), made video the basic media for educating and transmitting knowledge. However, IT tools are still not adequate to allow video content re-use, tagging, annotation and personalization. In this paper we analyze the problem of identifying coherent sequences, called scenes, in order to provide the users with a more manageable editing unit. A simple spectral clustering technique is proposed and compared with state-of-the-art results. We also discuss correct ways to evaluate the performance of automatic scene detection algorithms

Measuring scene detection performance

In this paper we evaluate the performance of scene detection techniques, starting from the classic precision/recall approach, moving to the better designed coverage/overflow measures, and finally proposing an improved metric, in order to solve frequently observed cases in which the numeric interpretation is different from the expected results. Numerical evaluation is performed on two recent proposals for automatic scene detection, and comparing them with a simple but effective novel approach. Experimental results are conducted to show how different measures may lead to different interpretations

Shape annotation for intelligent image retrieval

Annotation of shapes is an important process for semantic image retrieval. In this paper, we present a shape annotation framework that enables intelligent image retrieval by exploiting in a unified manner domain knowledge and perceptual description of shapes. A semi-supervised fuzzy clustering process is used to derive domain knowledge in terms of linguistic concepts referring to the semantic categories of shapes. For each category we derive a prototype that is a visual template for the category. A novel visual ontology is proposed to provide a description of prototypes and their salient parts. To describe parts of prototypes the visual ontology includes perceptual attributes that are defined by mimicking the analogy mechanism adopted by humans to describe the appearance of objects. The effectiveness of the developed framework as a facility for intelligent image retrieval is shown through results on a case study in the domain of fish shapes

Information fusion in content based image retrieval: A comprehensive overview

An ever increasing part of communication between persons involve the use of pictures, due to the cheap availability of powerful cameras on smartphones, and the cheap availability of storage space. The rising popularity of social networking applications such as Facebook, Twitter, Instagram, and of instant messaging applications, such as WhatsApp, WeChat, is the clear evidence of this phenomenon, due to the opportunity of sharing in real-time a pictorial representation of the context each individual is living in. The media rapidly exploited this phenomenon, using the same channel, either to publish their reports, or to gather additional information on an event through the community of users. While the real-time use of images is managed through metadata associated with the image (i.e., the timestamp, the geolocation, tags, etc.), their retrieval from an archive might be far from trivial, as an image bears a rich semantic content that goes beyond the description provided by its metadata. It turns out that after more than 20 years of research on Content-Based Image Retrieval (CBIR), the giant increase in the number and variety of images available in digital format is challenging the research community. It is quite easy to see that any approach aiming at facing such challenges must rely on different image representations that need to be conveniently fused in order to adapt to the subjectivity of image semantics. This paper offers a journey through the main information fusion ingredients that a recipe for the design of a CBIR system should include to meet the demanding needs of users

AXMEDIS 2008

The AXMEDIS International Conference series aims to explore all subjects and topics related to cross-media and digital-media content production, processing, management, standards, representation, sharing, protection and rights management, to address the latest developments and future trends of the technologies and their applications, impacts and exploitation. The AXMEDIS events offer venues for exchanging concepts, requirements, prototypes, research ideas, and findings which could contribute to academic research and also benefit business and industrial communities. In the Internet as well as in the digital era, cross-media production and distribution represent key developments and innovations that are fostered by emergent technologies to ensure better value for money while optimising productivity and market coverage

Desarrollo de una herramienta para la recuperación de videos en el dominio de las artes marciales mixtas

Con el continuo crecimiento de la subida de archivos multimedia a la Web, páginas conocidas como Youtube. Vimeo, entre otras; han estado realizando el continuo mejoramiento de sus Sistemas de Recuperación. De esta manera la cantidad de videos relevantes para el usuario no se verá afectada en la respuesta dada a este. Otros factores como el tipo de búsqueda (Sintáctica) realizada por estas páginas Web conforman esta problemática. Por esta razón, la solución que se plantea viene en base a la búsqueda Semántica, dado que al ser una búsqueda orientada a las relaciones entre conceptos se espera una mayor eficiencia que la obtenida por la búsqueda Sintáctica la cual se basa en que las palabras usadas en la búsqueda sean las mismas que se colocaron como etiquetas al archivo multimedia. Para llevar a cabo esta solución se realizó primero la Ontología, la cual posee los conocimientos que necesita la máquina acerca del dominio que ente caso son las Artes Marciales Mixtas. Para esto se debe tener un gran conocimiento del deporte, para reunir y relacionar los conceptos que lo conforman. Luego, se planificó como iba a actuar la herramienta, dado que hay varias opciones que se pueden realizar, con lo cual se decidió que el proceso comenzaría con un usuario haciendo una búsqueda textual, la cual nos permitirá conocer a que concepto del dominio pertenece y así a través de las relaciones de la Ontología encontrar otros conceptos relevantes; para luego consultarle a la base de datos cuales son los videos enlazados a estos conceptos. Al tener ya el proceso de funcionamiento, se realizaron tanto la base de datos que contendría en cada fila el URL de un video y la URI de un concepto (individuo) de la Ontología; de esta manera, se sabe qué contiene cada video. Para saber exactamente si mostraba todo el contenido del concepto se usaron flags. Finalmente se realizó el algoritmo que permite la búsqueda Semántica y se realizaron inferencias para búsquedas más específicas en el dominio. Teniendo la herramienta finalizada se realizaron pruebas de eficiencia usando 50 videos para lo cual se hallaron tanto la precisión como el recall de la herramienta al realizarse diversas consultas. Para esto se realizaron varias consultas para abarcar la mayoría de conceptos del domino y de esta manera tener resultados más confiable.Tesi