Image Information Retrieval based on Edge Responses, Shape and Texture Features using Datamining Techniques

The present paper proposes a new technique that extracts significant structural, texture and local edge features from images. The local features are extracted by a steady local edge response that can sustain the presence of noise, illumination changes. The local edge response image is converted in to a ternary pattern image based on a local threshold. The structural features are derived by extracting shapes in the form of textons. The texture features are derived by constructing grey level co-occurrence matrix (GLCM) on the derived texton image. A new variant of K-means clustering scheme is proposed for clustering of images. The proposed method is compared with various methods of image retrieval based on data mining techniques. The experimental results on Wang dataset shows the efficacy of the proposed method over the other methods

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

An efficient image retrieval scheme for colour enhancement of embedded and distributed surveillance images

From the past few years, the size of the data grows exponentially with respect to volume, velocity, and dimensionality due to wide spread use of embedded and distributed surveillance cameras for security reasons. In this paper, we have proposed an integrated approach for biometric-based image retrieval and processing which addresses the two issues. The first issue is related to the poor visibility of the images produced by the embedded and distributed surveillance cameras, and the second issue is concerned with the effective image retrieval based on the user query. This paper addresses the first issue by proposing an integrated image enhancement approach based on contrast enhancement and colour balancing methods. The contrast enhancement method is used to improve the contrast, while the colour balancing method helps to achieve a balanced colour. Importantly, in the colour balancing method, a new process for colour cast adjustment is introduced which relies on statistical calculation. It adjusts the colour cast and maintains the luminance of the image. The integrated image enhancement approach is applied to the enhancement of low quality images produced by surveillance cameras. The paper addresses the second issue relating to image retrieval by proposing a content-based image retrieval approach. The approach is based on the three features extraction methods namely colour, texture and shape. Colour histogram is used to extract the colour features of an image. Gabor filter is used to extract the texture features and the moment invariant is used to extract the shape features of an image. The use of these three algorithms ensures that the proposed image retrieval approach produces results which are highly relevant to the content of an image query, by taking into account the three distinct features of the image and the similarity metrics based on Euclidean measure. In order to retrieve the most relevant images, the proposed approach also employs a set of fuzzy heuristics to improve the quality of the results further. The result

A Hybrid data dependent dissimilarity measure for image retrieval

Abstract— In image retrieval, an effective dissimilarity measure is required to retrieve the perceptually similar images. Minkowski-type (lp ) distance is widely used for image retrieval, however it has its limitations. It focuses on distance between image features and ignores the data distribution of the image features, which can play an important role in measuring perceptual similarity of images. !! also favours the most dominant components in calculating the total dissimilarity. A data dependent measure, named !! -dissimilarity, which estimates the dissimilarity using the data distribution, has been proposed recently. Rather than relying on geometric distance, it measures the dissimilarity between two instances in each dimension as a probability mass in a region that encloses the two instances. It considers two instances in a sparse region to be more similar than in a dense region. Using the probability of data mass enables all the dimensions of feature vectors to contribute in the final estimate of dissimilarity, so it does not just heavily bias towards the most dominant components. However, relying only on data distribution and completely ignoring the geometric distance raise another limitation. This can result in finding two instances similar only due to being in a sparse region, however if the geometric distance between them is large then they are not perceptually similar. To address this limitation we proposed a new hybrid data dependent dissimilarity (HDDD) measure that considers both data distribution as well as geometric distance. Our experimental results using Corel database and Caltech 101 show that (HDDD) leads to higher image retrieval performance than lp distance (lpD) and mp

Convolution on the n-Sphere With Application to PDF Modeling

In this paper, we derive an explicit form of the convolution theorem for functions on an n-sphere. Our motivation comes from the design of a probability density estimator for n-dimensional random vectors. We propose a probability density function (pdf) estimation method that uses the derived convolution result on Sn. Random samples are mapped onto the n -sphere and estimation is performed in the new domain by convolving the samples with the smoothing kernel density. The convolution is carried out in the spectral domain. Samples are mapped between the n-sphere and the n-dimensional Euclidean space by the generalized stereographic projection. We apply the proposed model to several synthetic and real-world data sets and discuss the results

Privacy-Enhanced Query Processing in a Cloud-Based Encrypted DBaaS (Database as a Service)

In this dissertation, we researched techniques to support trustable and privacy enhanced solutions for on-line applications accessing to “always encrypted” data in remote DBaaS (data-base-as-a-service) or Cloud SQL-enabled backend solutions. Although solutions for SQL-querying of encrypted databases have been proposed in recent research, they fail in providing: (i) flexible multimodal query facilities includ ing online image searching and retrieval as extended queries to conventional SQL-based searches, (ii) searchable cryptographic constructions for image-indexing, searching and retrieving operations, (iii) reusable client-appliances for transparent integration of multi modal applications, and (iv) lack of performance and effectiveness validations for Cloud based DBaaS integrated deployments. At the same time, the study of partial homomorphic encryption and multimodal searchable encryption constructions is yet an ongoing research field. In this research direction, the need for a study and practical evaluations of such cryptographic is essential, to evaluate those cryptographic methods and techniques towards the materialization of effective solutions for practical applications. The objective of the dissertation is to design, implement and perform experimental evaluation of a security middleware solution, implementing a client/client-proxy/server appliance software architecture, to support the execution of applications requiring on line multimodal queries on “always encrypted” data maintained in outsourced cloud DBaaS backends. In this objective we include the support for SQL-based text-queries enhanced with searchable encrypted image-retrieval capabilities. We implemented a prototype of the proposed solution and we conducted an experimental benchmarking evaluation, to observe the effectiveness, latency and performance conditions in support ing those queries. The dissertation addressed the envisaged security middleware solution, as an experimental and usable solution that can be extended for future experimental testbench evaluations using different real cloud DBaaS deployments, as offered by well known cloud-providers.Nesta dissertação foram investigadas técnicas para suportar soluções com garantias de privacidade para aplicações que acedem on-line a dados que são mantidos sempre cifrados em nuvens que disponibilizam serviços de armazenamento de dados, nomeadamente soluções do tipo bases de dados interrogáveis por SQL. Embora soluções para suportar interrogações SQL em bases de dados cifradas tenham sido propostas anteriormente, estas falham em providenciar: (i) capacidade de efectuar pesquisas multimodais que possam incluir pesquisa combinada de texto e imagem com obtenção de imagens online, (ii) suporte de privacidade com base em construções criptograficas que permitam operações de indexacao, pesquisa e obtenção de imagens como dados cifrados pesquisáveis, (iii) suporte de integração para aplicações de gestão de dados em contexto multimodal, e (iv) ausência de validações experimentais com benchmarking dobre desempenho e eficiência em soluções DBaaS em que os dados sejam armazenados e manipulados na sua forma cifrada. A pesquisa de soluções de privacidade baseada em primitivas de cifras homomórficas parciais, tem sido vista como uma possível solução prática para interrogação de dados e bases de dados cifradas. No entanto, este é ainda um campo de investigação em desenvolvimento. Nesta direção de investigação, a necessidade de estudar e efectuar avaliações experimentais destas primitivas em bibliotecas de cifras homomórficas, reutilizáveis em diferentes contextos de aplicação e como solução efetiva para uso prático mais generalizado, é um aspeto essencial. O objectivo da dissertação e desenhar, implementar e efectuar avalições experimentais de uma proposta de solução middleware para suportar pesquisas multimodais em bases de dados mantidas cifradas em soluções de nuvens de armazenamento. Esta proposta visa a concepção e implementação de uma arquitectura de software client/client-proxy/server appliance para suportar execução eficiente de interrogações online sobre dados cifrados, suportando operações multimodais sobre dados mantidos protegidos em serviços de nuvens de armazenamento. Neste objectivo incluímos o suporte para interrogações estendidas de SQL, com capacidade para pesquisa e obtenção de dados cifrados que podem incluir texto e pesquisa de imagens por similaridade. Foi implementado um prototipo da solução proposta e foi efectuada uma avaliação experimental do mesmo, para observar as condições de eficiencia, latencia e desempenho do suporte dessas interrogações. Nesta avaliação incluímos a análise experimental da eficiência e impacto de diferentes construções criptográficas para pesquisas cifradas (searchable encryption) e cifras parcialmente homomórficas e que são usadas como componentes da solução proposta. A dissertaçao aborda a soluçao de seguranca projectada, como uma solução experimental que pode ser estendida e utilizavel para futuras aplcações e respetivas avaliações experimentais. Estas podem vir a adoptar soluções do tipo DBaaS, oferecidos como serviços na nuvem, por parte de diversos provedores ou fornecedores