Bio: Phillipe Salembier is an academic researcher. The author has contributed to research in topics: Content creation & Multimedia database. The author has an hindex of 1, co-authored 1 publications receiving 1292 citations.
01 Jun 2002
TL;DR: This book has been designed as a unique tutorial in the new MPEG 7 standard covering content creation, content distribution and content consumption, and presents a comprehensive overview of the principles and concepts involved in the complete range of Audio Visual material indexing, metadata description, information retrieval and browsing.
Abstract: From the Publisher: The MPEG standards are an evolving set of standards for video and audio compression. MPEG 7 technology covers the most recent developments in multimedia search and retreival, designed to standardise the description of multimedia content supporting a wide range of applications including DVD, CD and HDTV. Multimedia content description, search and retrieval is a rapidly expanding research area due to the increasing amount of audiovisual (AV) data available. The wealth of practical applications available and currently under development (for example, large scale multimedia search engines and AV broadcast servers) has lead to the development of processing tools to create the description of AV material or to support the identification or retrieval of AV documents. Written by experts in the field, this book has been designed as a unique tutorial in the new MPEG 7 standard covering content creation, content distribution and content consumption. At present there are no books documenting the available technologies in such a comprehensive way. Presents a comprehensive overview of the principles and concepts involved in the complete range of Audio Visual material indexing, metadata description, information retrieval and browsingDetails the major processing tools used for indexing and retrieval of images and video sequencesIndividual chapters, written by experts who have contributed to the development of MPEG 7, provide clear explanations of the underlying tools and technologies contributing to the standardDemostration software offering step-by-step guidance to the multi-media system components and eXperimentation model (XM) MPEG reference softwareCoincides with the release of the ISO standard in late 2001. A valuable reference resource for practising electronic and communications engineers designing and implementing MPEG 7 compliant systems, as well as for researchers and students working with multimedia database technology.
••02 Nov 2010
TL;DR: This work considers a standard non-spatial representation in which the frequencies but not the locations of quantized image features are used to discriminate between classes analogous to how words are used for text document classification without regard to their order of occurrence, and considers two spatial extensions.
Abstract: We investigate bag-of-visual-words (BOVW) approaches to land-use classification in high-resolution overhead imagery. We consider a standard non-spatial representation in which the frequencies but not the locations of quantized image features are used to discriminate between classes analogous to how words are used for text document classification without regard to their order of occurrence. We also consider two spatial extensions, the established spatial pyramid match kernel which considers the absolute spatial arrangement of the image features, as well as a novel method which we term the spatial co-occurrence kernel that considers the relative arrangement. These extensions are motivated by the importance of spatial structure in geographic data.The methods are evaluated using a large ground truth image dataset of 21 land-use classes. In addition to comparisons with standard approaches, we perform extensive evaluation of different configurations such as the size of the visual dictionaries used to derive the BOVW representations and the scale at which the spatial relationships are considered.We show that even though BOVW approaches do not necessarily perform better than the best standard approaches overall, they represent a robust alternative that is more effective for certain land-use classes. We also show that extending the BOVW approach with our proposed spatial co-occurrence kernel consistently improves performance.
TL;DR: The aim, methodologies, and broad details of the MPEG-7 standard development forVisual content description for visual content description are outlined.
Abstract: The MPEG-7 visual standard under development specifies content-based descriptors that allow users or agents (or search engines) to measure similarity in images or video based on visual criteria, and can be used to efficiently identify, filter, or browse images or video based on visual content. More specifically, MPEG-7 specifies color, texture, object shape, global motion, or object motion features for this purpose. This paper outlines the aim, methodologies, and broad details of the MPEG-7 standard development for visual content description.
TL;DR: This paper analyzes key aspects of the various AIA methods, including both feature extraction and semantic learning methods and provides a comprehensive survey on automatic image annotation.
Abstract: Nowadays, more and more images are available. However, to find a required image for an ordinary user is a challenging task. Large amount of researches on image retrieval have been carried out in the past two decades. Traditionally, research in this area focuses on content based image retrieval. However, recent research shows that there is a semantic gap between content based image retrieval and image semantics understandable by humans. As a result, research in this area has shifted to bridge the semantic gap between low level image features and high level semantics. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) which extracts semantic features using machine learning techniques. In this paper, we focus on this latest development in image retrieval and provide a comprehensive survey on automatic image annotation. We analyse key aspects of the various AIA methods, including both feature extraction and semantic learning methods. Major methods are discussed and illustrated in details. We report our findings and provide future research directions in the AIA area in the conclusions
TL;DR: Experimental results demonstrate that the novel color difference histograms (CDH) method is much more efficient than the existing image feature descriptors that were originally developed for content-based image retrieval, such as MPEG-7 edge histogram descriptors, color autocorrelograms and multi-texton histograms.
Abstract: This paper presents a novel image feature representation method, namely color difference histograms (CDH), for image retrieval. This method is entirely different from the existing histograms; most of the existing histogram techniques merely count the number or frequency of pixels. However, the unique characteristic of CDHs is that they count the perceptually uniform color difference between two points under different backgrounds with regard to colors and edge orientations in L*a*b* color space. This method pays more attention to color, edge orientation and perceptually uniform color differences, and encodes color, orientation and perceptually uniform color difference via feature representation in a similar manner to the human visual system. The method can be considered as a novel visual attribute descriptor combining edge orientation, color and perceptually uniform color difference, as well as taking the spatial layout into account without any image segmentation, learning processes or clustering implementation. Experimental results demonstrate that it is much more efficient than the existing image feature descriptors that were originally developed for content-based image retrieval, such as MPEG-7 edge histogram descriptors, color autocorrelograms and multi-texton histograms. It has a strong discriminative power using the color, texture and shape features while accounting for spatial layout.
TL;DR: A benchmark for evaluating the performance of large-scale sketch-based image retrieval systems is introduced and new descriptors based on the bag-of-features approach are developed that significantly outperform other descriptors in the literature.
Abstract: We introduce a benchmark for evaluating the performance of large-scale sketch-based image retrieval systems. The necessary data are acquired in a controlled user study where subjects rate how well given sketch/image pairs match. We suggest how to use the data for evaluating the performance of sketch-based image retrieval systems. The benchmark data as well as the large image database are made publicly available for further studies of this type. Furthermore, we develop new descriptors based on the bag-of-features approach and use the benchmark to demonstrate that they significantly outperform other descriptors in the literature.