How to Cope with the Performance Gap in Content-Based Image Retrieval Systems

Traina, Agma J. M.; Traina, Caetano; Ciferri, Cristina Dutra de Aguiar; Ribeiro, Marcela Xavier; Azevedo-Marques, Paulo Mazzoncini de

doi:10.4018/jhisi.2009010104

Cited by 7 publications

(6 citation statements)

References 64 publications

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“…We found experimentally using the FastBit software [24] that the interval encoding provided better performance results when compared to the intervalequality encoding and the binary encoding for indexing the current imageDW. We also found experimentally that 5 bins is the best value among the values of 5,10,15,20,25,30,35,40,45, and 50 bins for the current imageDW.…”

Section: Methodsmentioning

confidence: 87%

“…Thus, another interesting functionalities that could be provided by the tool are: (i) to show the similar images that were used to calculate the query answer, and (ii) to apply relevance feedback techniques over these similar images to take the preferences of the specialists into account (e.g. [34,35]). That is, we have successfully developed a technical infrastructure and will look into integration and usability issues next.…”

Section: Discussionmentioning

confidence: 99%

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A similarity-based data warehousing environment for medical images

Teixeira

Annibal

Felipe

et al. 2015

Computers in Biology and Medicine

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A core issue of the decision-making process in the medical field is to support the execution of analytical (OLAP) similarity queries over images in data warehousing environments. In this paper, we focus on this issue. We propose imageDWE, a non-conventional data warehousing environment that enables the storage of intrinsic features taken from medical images in a data warehouse and supports OLAP similarity queries over them. To comply with this goal, we introduce the concept of perceptual layer, which is an abstraction used to represent an image dataset according to a given feature descriptor in order to enable similarity search. Based on this concept, we propose the imageDW, an extended data warehouse with dimension tables specifically designed to support one or more perceptual layers. We also detail how to build an imageDW and how to load image data into it. Furthermore, we show how to process OLAP similarity queries composed of a conventional predicate and a similarity search predicate that encompasses the specification of one or more perceptual layers. Moreover, we introduce an index technique to improve the OLAP query processing over images. We carried out performance tests over a data warehouse environment that consolidated medical images from exams of several modalities. The results demonstrated the feasibility and efficiency of our proposed imageDWE to manage images and to process OLAP similarity queries. The results also demonstrated that the use of the proposed index technique guaranteed a great improvement in query processing.

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Section: Methodsmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 99%

A similarity-based data warehousing environment for medical images

Teixeira

Annibal

Felipe

et al. 2015

Computers in Biology and Medicine

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show abstract

“…Consequentemente, valores maiores são retornados para elementos muito distintos. A função δ sim definida para o cálculo de dissimilaridade entre pares de elementos deve garantir algumas propriedades interessantes para permitir que técnicas de indexação possam ser usadas para agilizar as operações de busca por similaridade (Güld et al, 2007;Traina et al, 2009).…”

Section: Medidas De Similaridadeunclassified

Explorando variedade em consultas por similaridade

Santos¹

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À minha esposa Ailana, com amor, admiração e gratidão por sua compreensão, carinho, presença e incansável apoio ao longo do período de elaboração deste trabalho. À minha filha Bianca, por cada sorriso e por sua compreensão nos momentos de ausência. i Agradecimentos À Deus. Ao meu orientador, Prof. Dr. Caetano Traina Júnior, agradeço o apoio e a confiança que dispensou durante a elaboração deste trabalho, mesmo nos momentos que eu não acreditava. À Prof a Dr a Agma Juci Machado Traina, devido aos conselhos e sugestões. À Prof a Dr a Elaine Parros Machado de Sousa, por ter conduzido o encontro com o Prof. Caetano. À minha querida esposa, Ailana, e a minha filha, Bianca, pelo amor, apoio incondicional, carinho e paciência me fortalecendo nos momentos difíceis. Aos meus pais, pelo amor, carinho, dedicação e exemplo em todos os momentos da minha vida. Aos meus sogros, pela maravilhosa família da qual tenho a graça de pertencer. Aos grandes amigos que fiz no GBdI, em especial os que tive o prazer de discutir ideias e risadas:

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“…No contexto de sistemas CBIR, um dos maiores desafiosé a redução do gap semântico em consultas por similaridade, ou seja, a diferença entre o resultado produzido pelo sistema e o que era esperado pelos usuários (Traina et al, 2009). Isso ocorre, pois diferentes usuários podem escolher condições distintas para determinar a similaridade em um conjunto de dados complexo.…”

Section: Camadas Perceptuaisunclassified

Processamento de consultas analíticas com predicados de similaridade entre imagens em ambientes de data warehousing

Teixeira¹

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A data warehousing environment offers support to the decision-making process. It consolidates data from distributed, autonomous and heterogeneous information sources into one of its main components, the data warehouse. Furthermore, it provides efficient processing of analytical queries (i.e. OLAP queries). A conventional data warehouse stores only alphanumeric data. On the other hand, an image data warehouse stores not only alphanumeric data but also intrinsic features of images, thus allowing data warehousing environments to perform analytical similarity queries over images. This requires the development of strategies to provide efficient processing of these complex and costly queries. Although there are a number of approaches in the literature aimed at the development of bitmap index for data warehouses and metric access methods for the efficient processing of similarity queries over images, to the best of our knowledge, there is not an approach that investigate these two issues in the same setting. In this research, we fill this gap in the literature by introducing the following main contributions: (i) the proposal of the ImageDW-index, an optimization mechanism aimed at the efficient processing of analytical queries extended with similarity predicates over images; and (ii) definition of different processing strategies for image data warehouses using the ImageDW-index. In order to validate these main proposals, we also introduce two secondary contributions, as follows: (iii) the ImageDW-Gen, a data generator to populate image data warehouses; and (iv) the proposal of four query classes, each one enforcing different query processing costs associated to the similarity predicates in image data warehousing environments. Using the ImageDW-Gen, performance tests were carried out in order to investigate the advantages introduced by the proposed strategies, according to the query classes. Compared to the most related work available in the literature, the ImageDW-index provided a performance gain that varied from 55.57% to 82.16%, considering one of the proposed strategies.

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How to Cope with the Performance Gap in Content-Based Image Retrieval Systems

Cited by 7 publications

References 64 publications

A similarity-based data warehousing environment for medical images

A similarity-based data warehousing environment for medical images

Explorando variedade em consultas por similaridade

Processamento de consultas analíticas com predicados de similaridade entre imagens em ambientes de data warehousing

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