Image mining: trends and developments

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining

Image mining: issues, frameworks and techniques

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. Despite the development of many applications and algorithms in the individual research fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper

An information-driven framework for image mining

[Abstract]: Image mining systems that can automatically extract semantically meaningful information (knowledge) from image data are increasingly in demand. The fundamental challenge in image mining is to determine how low-level, pixel representation contained in a raw image or image sequence can be processed to identify high-level spatial objects and relationships. To meet this challenge, we propose an efficient information-driven framework for image mining. We distinguish four levels of information: the Pixel Level, the Object Level, the Semantic Concept Level, and the Pattern and Knowledge Level. High-dimensional indexing schemes and retrieval techniques are also included in the framework to support the flow of information among the levels. We believe this framework represents the first step towards capturing the different levels of information present in image data and addressing the issues and challenges of discovering useful patterns/knowledge from each level

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Sculptures de la Gaule Romaine. Bases NEsp et RBR : une gestion de la mémoire collective

International audienceSince the irreplaceable inventory of Émile Espérandieu, completed by Raymond Lantier, the sculptures collections of Roman Gaule have been rarely and insufficiently exposed. The publication of the Nouvel Espérandieu enables to reveal the discoveries since 1966 and - above all - to launch an exceptional documentary operation with old and new documents. Humble or spectacular objects are checked off, digitized and indexed. NEsp and RBR databases will really make available these data to a large spectrum of public, thank to an important effort of data enrichment and valorisation by developing very efficient technologies to perform the multimedia and multilingual indexing of the databases. For image data, shape based and texture based algorithms and face detection and recognition techniques will be used. The multilingual dimension of the data indexing enables an international valorisation of the data. The quality and richness of the data, the efficient structure and architecture of the global system could make NEsp-RBR a norm or standard for other databasesA la suite de l'inventaire irremplaçable de Émile Espérandieu, complété par Raymond Lantier, la publication du "Nouvel Espérandieu" donne accès aux collections de sculptures de la Gaule romaine. Il a permis la mise en place d'un vaste programme complémentaire d'inventaire, de numérisation et de documentation des objets sculptés, rassemblés dans les bases NEsp et RBR. S'appuyant sur l'enrichissement des données, une indexation multilingue et le respect des normes de description des objets archéologiques, ces bases offrent un outil de valorisation international