Image Content Engine (ICE): A System for Fast Image Database Searches

The Image Content Engine (ICE) is being developed to provide cueing assistance to human image analysts faced with increasingly large and intractable amounts of image data. The ICE architecture includes user configurable feature extraction pipelines which produce intermediate feature vector and match surface files which can then be accessed by interactive relational queries. Application of the feature extraction algorithms to large collections of images may be extremely time consuming and is launched as a batch job on a Linux cluster. The query interface accesses only the intermediate files and returns candidate hits nearly instantaneously. Queries may be posed for individual objects or collections. The query interface prompts the user for feedback, and applies relevance feedback algorithms to revise the feature vector weighting and focus on relevant search results. Examples of feature extraction and both model-based and search-by-example queries are presented

Reconhecimento de Padrões de Texturas em Imagens Digitais Usando uma Rede Neural Artificial Híbrida

Este trabalho apresenta um mecanismo de indexação de imagens baseado em características texturais utilizando redes neurais artificiais. Os atributos que descrevem as texturas e que são utilizados para classificá-las advêm dos descritores de Haralick os quais são baseados em matrizes de coocorrência. Uma rede neural híbrida é utilizada para reconhecer os diferentes padrões de texturas. Primeiramente, é realizado um agrupamento inicial dos padrões por um modelo não supervisionado (Mapas Auto-organizáveis) e numa segunda fase, utiliza-se o modelo supervisionado (Quantização Vetorial por Aprendizagem) para melhorar a segmentação das classes de padrões previamente agrupados pelo modelo não supervisionado.&nbsp

Reconhecimento de Padrões de Texturas em Imagens Digitais Usando uma Rede Neural Artificial Híbrida

Este trabalho apresenta um mecanismo de indexação de imagens baseado em características texturais utilizando redes neurais artificiais. Os atributos que descrevem as texturas e que são utilizados para classificá-las advêm dos descritores de Haralick os quais são baseados em matrizes de coocorrência. Uma rede neural híbrida é utilizada para reconhecer os diferentes padrões de texturas. Primeiramente, é realizado um agrupamento inicial dos padrões por um modelo não supervisionado (Mapas Auto-organizáveis) e numa segunda fase, utiliza-se o modelo supervisionado (Quantização Vetorial por Aprendizagem) para melhorar a segmentação das classes de padrões previamente agrupados pelo modelo não supervisionado.

Image retrieval using the combination of text-based and content-based algorithms

Image retrieval is an important research field which has received great attention in the last decades. In this paper, we present an approach for the image retrieval based on the combination of text-based and content-based features. For text-based features, keywords and for content-based features, color and texture features have been used. Query in this system contains some keywords and an input image. At first, the images are retrieved based on the input keywords. Then, visual features are extracted to retrieve ideal output images. For extraction of color features we have used color moments and for texture we have used color co-occurrence matrix. The COREL image database have been used for our experimental results. The experimental results show that the performance of the combination of both text- and content- based features is much higher than each of them which is applied separately

A fast compression-based similarity measure with applications to content-based image retrieval

Compression-based similarity measures are effectively employed in applications on diverse data types with a basically parameter-free approach. Nevertheless, there are problems in applying these techniques to medium-to-large datasets which have been seldom addressed. This paper proposes a similarity measure based on compression with dictionaries, the Fast Compression Distance (FCD), which reduces the complexity of these methods, without degradations in performance. On its basis a content-based color image retrieval system is defined, which can be compared to state-of-the-art methods based on invariant color features. Through the FCD a better understanding of compression-based techniques is achieved, by performing experiments on datasets which are larger than the ones analyzed so far in literature

Outdoor view recognition based on landmark grouping and logistic regression

Vision-based robot localization outdoors has remained more elusive than its indoors counterpart. Drastic illumination changes and the scarceness of suitable landmarks are the main difficulties. This paper attempts to surmount them by deviating from the main trend of using local features. Instead, a global descriptor called landmark-view is defined, which aggregates the most visually-salient landmarks present in each scene. Thus, landmark co-occurrence and spatial and saliency relationships between them are added to the single landmark characterization, based on saliency and color distribution. A suitable framework to compare landmark-views is developed, and it is shown how this remarkably enhances the recognition performance, compared against single landmark recognition. A view-matching model is constructed using logistic regression. Experimentation using 45 views, acquired outdoors, containing 273 landmarks, yielded good recognition results. The overall percentage of correct view classification obtained was 80.6%, indicating the adequacy of the approach.Peer ReviewedPostprint (author’s final draft

Parallel biometrics computing using mobile agents

We present an efficient and effective approach to personal identification by parallel biometrics computing using mobile agents. To overcome the limitations of the existing password-based authentication services on the Internet, we integrate multiple personal features including fingerprints, palmprints, hand geometry and face into a hierarchical structure for fast and reliable personal identification and verification. To increase the speed and flexibility of the process, we use mobile agents as a navigational tool for parallel implementation in a distributed environment, which includes hierarchical biometric feature extraction, multiple feature integration, dynamic biometric data indexing and guided search. To solve the problems associated with bottlenecks and platform dependence, we apply a four-layered structural model and a three-dimensional operational model to achieve high performance. Instead of applying predefined task scheduling schemes to allocate the computing resources, we introduce a new online competitive algorithm to guide the dynamic allocation of mobile agents with greater flexibility. The experimental results demonstrate the feasibility and the potential of the proposed methodDepartment of ComputingRefereed conference pape

Image Shape Clasification Using Computational Intelligence and Object Orientation

Master of Science in Engineering - Electrical and Information EngineeringWith the increase in complexity of modern software systems, there is a great demand for software engineering techniques. Calculation processes are becoming more and more complex, especially in the field of machine vision and computational intelligence. A suitable object oriented calculation process framework is developed in order to address this problem. To demonstrate the effectiveness of the framework, a simple shape classification system is implemented in C#. A suitable method for representing shapes of images is developed and it is used for classification by a neural network. Sets of real-world images of hands and automobiles are used to test the system. The performance of the object oriented system in C# is compared to a functional paradigm system in Matlab and it is found that object orientation is well suited to the later stages of machine vision while the functional approach is well suited to low level image processing tasks

An extension of local mesh peak valley edge based feature descriptor for image retrieval in bio-medical images

Various texture based approaches have been proposed for image indexing in bio-medical image processing and a precise description of image for indexing in bio-medical image database has always been a challenging task. In this paper, an extension of local mesh peak valley edge pattern (LMePVEP) has been proposed and its effectiveness is experimentally justified. The proposed algorithm explores the relationship of center pixel with the surrounding ones along with the relationship of pixels amongst each other in five different directions. It is then compared with the original LMePVEP as well as a directional local ternary quantized extrema pattern (DLTerQEP) based approach using two bench mark databases viz. ELCAP database for lungs and Wiki cancer data set for thyroid cancer. Further a live dataset for brain tumor is also used for experimental evaluation. The experimental results show that an average improvement of 11.16% in terms of average retrieval rate (ARR) and 5.37% in terms of average retrieval precision (ARP) is observed for proposed enhanced LMePVEP over conventional LMePVEP