A new method for feature selection based on fuzzy similarity measures using multi objective genetic algorithm

Feature selection (FS) is considered to be an important preprocessing step in machine learning and pattern recognition, and feature evaluation is the key issue for constructing a feature selection algorithm. Feature selection process can also reduce noise and this way enhance the classification accuracy. In this article, feature selection method based on fuzzy similarity measures by multi objective genetic algorithm (FSFSM - MOGA) is introduced and performance of the proposed method on published data sets from UCI was evaluated. The results show the efficiency of the method is compared with the conventional version. When this method multi-objective genetic algorithms and fuzzy similarity measures used in CFS method can improve it

An Information-Theoretic Measure For Face Recognition: Comparison With Structural Similarity

Automatic recognition of people faces is a challenging problem that has received significant attention from signal processing researchers in recent years. This is due to its several applications in different fields, including security and forensic analysis. Despite this attention, face recognition is still one among the most challenging problems. Up to this moment, there is no technique that provides a reliable solution to all situations. In this paper a novel technique for face recognition is presented. This technique, which is called ISSIM, is derived from our recently published information - theoretic similarity measure HSSIM, which was based on joint histogram. Face recognition with ISSIM is still based on joint histogram of a test image and a database images. Performance evaluation was performed on MATLAB using part of the well-known AT&T image database that consists of 49 face images, from which seven subjects are chosen, and for each subject seven views (poses) are chosen with different facial expressions. The goal of this paper is to present a simplified approach for face recognition that may work in real-time environments. Performance of our information - theoretic face recognition method (ISSIM) has been demonstrated experimentally and is shown to outperform the well-known, statistical-based method (SSIM)

Interactive Visualization of Multidimensional Feature Spaces

Image similarity models characterize images as points in high-dimensional feature spaces. Each point is represented by a combination of distinct features, such as brightness, color histograms or texture characteristics of the image, etc. For the design and tuning of features, and thus the effectiveness of the image similarity model, it is important to understand the interrelations of individual features and the implications on the structure of the feature space. In this paper, we discuss an interactive visualization tool for the exploration of multidimensional feature spaces. Our tool uses a graph as an intermediate representation of the points in the feature space. A mass spring algorithm is used to layout the graph in a 2D space in which arrangements of similar images are attracted to each other and dissimilar images are repelled. The emphasis of the visualization tool is on interaction: users may influence the layout by interactively scaling dimensions of the feature space. In this way, the user can explore how a feature behaves in relation to other features

A geometric dissimilarity criterion between Jordan spatial mosaics. Theoretical aspects and application to segmentation evaluation

International audienceAn image segmentation process often results in a special spatial set, called a mosaic, as the subdivision of a domain S within the n-dimensional Euclidean space. In this paper, S will be a compact domain and the study will be focused on finite Jordan mosaics, that is to say mosaics with a finite number of regions and where the boundary of each region is a Jordan hypersurface. The first part of this paper addresses the problem of comparing a Jordan mosaic to a given reference Jordan mosaic and introduces the (Epsilon) dissimilarity criterion. The second part will show that the (Epsilon) dissimilarity criterion can be used to perform the evaluation of image segmentation processes. It will be compared to classical criterions in regard to several geometric transformations. The pros and cons of these criterions are presented and discussed, showing that the dissimilarity criterion outperforms the other ones

Multimedia

The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications

Um modelo para recuperação por conteudo de imagens de sensoriamento remoto

Orientador: Neucimar Jeronimo LeiteTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O problema da recuperação de imagens por conteúdo tem sido uma área de muito interesse nos últimos anos, com múltiplas aplicações em diferentes domínios de geração de imagens. Uma classe de imagem onde este problema não tem sido resolvido satisfatoriamente referese à classe de Sensoriamento Remoto. Imagens de Sensoriamento Remoto (ISR) são obtidas como combinação do sensoriamento da Terra em múltiplas bandas espectrais. Esta tese aborda o problema da recuperação por em conteúdo das ISR . Este tipo de recuperação parte da caracterização do conteúdo de uma imagem e uma das suas principais abordagens considera modelos matemáticos da área de Processamento de Imagens a ser abordada nesta tese. Neste trabalho, abordamos o processo de recuperação de ISR que utilizando três recursos principais: padrões de textura e cor como elemento básico da consulta, uso de múltiplos modelos matemáticos de representação e caracterização do conteúdo e um mecanismo de retroalimentação para o processo de consulta. As principais contribuições da tese são: (1) uma análise dos problemas da recuperação por conteúdo para ISR; (2) a proposta de um modelo para esta recuperação; (3) um modelo e métrica de similaridade baseado no modelo proposto; (4) proposta de implementação do processamento das consultas que mostra a viabilidade do modeloAbstract: Content-based retrieval of images is a topic of growing interest given us multiple applications. One kind of images that have not yet been dealt with satisfactorily are the so-called Remote Sensing Images. Remote Sensing Images (RSI) are a especial type of image, created by combination of sensoring on different spectral bands . This work deals with the problem of content-based retrieval of Remote Sensiong Images(RSI). It uses the image retrieval approach based on content representation models from image processing area. This work presents a content-based image retrieval model for RSI, based on three main features: patterns of color and texture as basic query concept, use of multiple content representation modelsanda feedback Televance machanism. The main contributions os these work are: (1) an analysis of content-based RSI pro,. blems; (2) a proposal of a model for RSI retrieval; (3) a proposal of model and metric for similarity measure; (4) a proposal of algorithm for processing of content-based queriesDoutoradoDoutor em Ciência da Computaçã

Efficient similarity-based operations for data integration

Similarity-based operations, similarity join, similarity grouping, data integrationMagdeburg, Univ., Fak. für Informatik, Diss., 2004von Eike Schalleh

Semantics of video shots for content-based retrieval

Content-based video retrieval research combines expertise from many different areas, such as signal processing, machine learning, pattern recognition, and computer vision. As video extends into both the spatial and the temporal domain, we require techniques for the temporal decomposition of footage so that specific content can be accessed. This content may then be semantically classified - ideally in an automated process - to enable filtering, browsing, and searching. An important aspect that must be considered is that pictorial representation of information may be interpreted differently by individual users because it is less specific than its textual representation. In this thesis, we address several fundamental issues of content-based video retrieval for effective handling of digital footage. Temporal segmentation, the common first step in handling digital video, is the decomposition of video streams into smaller, semantically coherent entities. This is usually performed by detecting the transitions that separate single camera takes. While abrupt transitions - cuts - can be detected relatively well with existing techniques, effective detection of gradual transitions remains difficult. We present our approach to temporal video segmentation, proposing a novel algorithm that evaluates sets of frames using a relatively simple histogram feature. Our technique has been shown to range among the best existing shot segmentation algorithms in large-scale evaluations. The next step is semantic classification of each video segment to generate an index for content-based retrieval in video databases. Machine learning techniques can be applied effectively to classify video content. However, these techniques require manually classified examples for training before automatic classification of unseen content can be carried out. Manually classifying training examples is not trivial because of the implied ambiguity of visual content. We propose an unsupervised learning approach based on latent class modelling in which we obtain multiple judgements per video shot and model the users' response behaviour over a large collection of shots. This technique yields a more generic classification of the visual content. Moreover, it enables the quality assessment of the classification, and maximises the number of training examples by resolving disagreement. We apply this approach to data from a large-scale, collaborative annotation effort and present ways to improve the effectiveness for manual annotation of visual content by better design and specification of the process. Automatic speech recognition techniques along with semantic classification of video content can be used to implement video search using textual queries. This requires the application of text search techniques to video and the combination of different information sources. We explore several text-based query expansion techniques for speech-based video retrieval, and propose a fusion method to improve overall effectiveness. To combine both text and visual search approaches, we explore a fusion technique that combines spoken information and visual information using semantic keywords automatically assigned to the footage based on the visual content. The techniques that we propose help to facilitate effective content-based video retrieval and highlight the importance of considering different user interpretations of visual content. This allows better understanding of video content and a more holistic approach to multimedia retrieval in the future