Bridging the semantic gap in content-based image retrieval.

To manage large image databases, Content-Based Image Retrieval (CBIR) emerged as a new research subject. CBIR involves the development of automated methods to use visual features in searching and retrieving. Unfortunately, the performance of most CBIR systems is inherently constrained by the low-level visual features because they cannot adequately express the user\u27s high-level concepts. This is known as the semantic gap problem. This dissertation introduces a new approach to CBIR that attempts to bridge the semantic gap. Our approach includes four components. The first one learns a multi-modal thesaurus that associates low-level visual profiles with high-level keywords. This is accomplished through image segmentation, feature extraction, and clustering of image regions. The second component uses the thesaurus to annotate images in an unsupervised way. This is accomplished through fuzzy membership functions to label new regions based on their proximity to the profiles in the thesaurus. The third component consists of an efficient and effective method for fusing the retrieval results from the multi-modal features. Our method is based on learning and adapting fuzzy membership functions to the distribution of the features\u27 distances and assigning a degree of worthiness to each feature. The fourth component provides the user with the option to perform hybrid querying and query expansion. This allows the enrichment of a visual query with textual data extracted from the automatically labeled images in the database. The four components are integrated into a complete CBIR system that can run in three different and complementary modes. The first mode allows the user to query using an example image. The second mode allows the user to specify positive and/or negative sample regions that should or should not be included in the retrieved images. The third mode uses a Graphical Text Interface to allow the user to browse the database interactively using a combination of low-level features and high-level concepts. The proposed system and ail of its components and modes are implemented and validated using a large data collection for accuracy, performance, and improvement over traditional CBIR techniques

LEARNING INTERACTION MEASURE WITH RELEVANCE FEEDBACK IN IMAGE RETRIEVAL

Relevance feedback is an eective approach to bridge the gap between low-level featureextraction and high-level semantic concept in content-based image retrieval (CBIR). In this paper,we further improve the use of users feedback with multi-feature query and the Choquet integral.Taking into account the interaction among feature sets, feedback information are used to adjust thefeature's relevance weights that are considered as the fuzzy density values in the Choquet integralto dene the overall similarity measure between two images. The feature weight adjustment andintegration aims at minimizing the dierence between users desire and outcome of the retrieval system.Experimental results on several benchmark datasets have shown the eectiveness of the proposedmethod in improving the quality of CBIR systems

L'intégrale de Choquet discrète pour l'agrégation de pertinence multidimensionnelle

International audienceDans ce papier, nous nous intéressons à étudier le problème de l'agrégation multicritères dans le domaine de la recherche d'information (RI). Nous proposons une nouvelle approche basée sur l'intégrale de Choquet pour l'agrégation de pertinence multidimensionnelle. La principale originalité de cet opérateur, outre sa capacité à modéliser des interactions entre les différentes dimensions de pertinence, est sa capacité à généraliser de nombreuses fonctions d'agrégation classiques. L'évaluation de l'efficacité de notre approche est effectuée dans une tâche de recherche de tweets, où les critères conjointement utilisés sont, la pertinence thématique, l'autorité et la fraîcheur. Les résultats expérimentaux obtenus sur la collection de test fournie par la tâche Microblog de TREC 2011 montrent la pertinence de notre proposition

A feature selection method based on Choquet Integral and Typicality Analysis

ISBN: 1-4244-1210-2International audienceIn this paper, we present an iterative feature selection method based on feature typicality and interactivity analysis. The aim of such a method is to enhance model interpretability by selecting the best significant features among a list extracted from images. The inference mechanism uses a fuzzy linguistic rule-based system. In the presented application, we apply this method to wood defect classification. Nowadays, feature selection is expertise-driven and most of the time, expert uses features by habits which not always represent the best ones to use. The proposed method aims to replace expert selection by automatically choosing the most adapted features to the recognition problem

Reconnaissance de symboles graphiques par le biais de l'intégrale de Choquet

International audiencenous présentons dans cet article trois modèles pour extraire un sous-ensemble de règles de décision et leur agrégation en une règle de décision unique par le biais de l'intégrale de Choquet. Nous nous intéressons à des applications où l'on possède peu d'échantillons représentatifs par classe. Les approches que nous avons construites sont de type global, par classe ou bi-classes. Des applications sur des données réelles attestent de la robustesse de nos méthodes et leur adaptabilité

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

Intelligent Image Retrieval Techniques: A Survey

AbstractIn the current era of digital communication, the use of digital images has increased for expressing, sharing and interpreting information. While working with digital images, quite often it is necessary to search for a specific image for a particular situation based on the visual contents of the image. This task looks easy if you are dealing with tens of images but it gets more difficult when the number of images goes from tens to hundreds and thousands, and the same content-based searching task becomes extremely complex when the number of images is in the millions. To deal with the situation, some intelligent way of content-based searching is required to fulfill the searching request with right visual contents in a reasonable amount of time. There are some really smart techniques proposed by researchers for efficient and robust content-based image retrieval. In this research, the aim is to highlight the efforts of researchers who conducted some brilliant work and to provide a proof of concept for intelligent content-based image retrieval techniques

Preference Learning

This report documents the program and the outcomes of Dagstuhl Seminar 14101 “Preference Learning”. Preferences have recently received considerable attention in disciplines such as machine learning, knowledge discovery, information retrieval, statistics, social choice theory, multiple criteria decision making, decision under risk and uncertainty, operations research, and others. The motivation for this seminar was to showcase recent progress in these different areas with the goal of working towards a common basis of understanding, which should help to facilitate future synergies

WFIRST Coronagraph Technology Requirements: Status Update and Systems Engineering Approach

The coronagraphic instrument (CGI) on the Wide-Field Infrared Survey Telescope (WFIRST) will demonstrate technologies and methods for high-contrast direct imaging and spectroscopy of exoplanet systems in reflected light, including polarimetry of circumstellar disks. The WFIRST management and CGI engineering and science investigation teams have developed requirements for the instrument, motivated by the objectives and technology development needs of potential future flagship exoplanet characterization missions such as the NASA Habitable Exoplanet Imaging Mission (HabEx) and the Large UV/Optical/IR Surveyor (LUVOIR). The requirements have been refined to support recommendations from the WFIRST Independent External Technical/Management/Cost Review (WIETR) that the WFIRST CGI be classified as a technology demonstration instrument instead of a science instrument. This paper provides a description of how the CGI requirements flow from the top of the overall WFIRST mission structure through the Level 2 requirements, where the focus here is on capturing the detailed context and rationales for the CGI Level 2 requirements. The WFIRST requirements flow starts with the top Program Level Requirements Appendix (PLRA), which contains both high-level mission objectives as well as the CGI-specific baseline technical and data requirements (BTR and BDR, respectively)... We also present the process and collaborative tools used in the L2 requirements development and management, including the collection and organization of science inputs, an open-source approach to managing the requirements database, and automating documentation. The tools created for the CGI L2 requirements have the potential to improve the design and planning of other projects, streamlining requirement management and maintenance. [Abstract Abbreviated]Comment: 16 pages, 4 figure