Unsupervised Graph-based Rank Aggregation for Improved Retrieval

This paper presents a robust and comprehensive graph-based rank aggregation approach, used to combine results of isolated ranker models in retrieval tasks. The method follows an unsupervised scheme, which is independent of how the isolated ranks are formulated. Our approach is able to combine arbitrary models, defined in terms of different ranking criteria, such as those based on textual, image or hybrid content representations. We reformulate the ad-hoc retrieval problem as a document retrieval based on fusion graphs, which we propose as a new unified representation model capable of merging multiple ranks and expressing inter-relationships of retrieval results automatically. By doing so, we claim that the retrieval system can benefit from learning the manifold structure of datasets, thus leading to more effective results. Another contribution is that our graph-based aggregation formulation, unlike existing approaches, allows for encapsulating contextual information encoded from multiple ranks, which can be directly used for ranking, without further computations and post-processing steps over the graphs. Based on the graphs, a novel similarity retrieval score is formulated using an efficient computation of minimum common subgraphs. Finally, another benefit over existing approaches is the absence of hyperparameters. A comprehensive experimental evaluation was conducted considering diverse well-known public datasets, composed of textual, image, and multimodal documents. Performed experiments demonstrate that our method reaches top performance, yielding better effectiveness scores than state-of-the-art baseline methods and promoting large gains over the rankers being fused, thus demonstrating the successful capability of the proposal in representing queries based on a unified graph-based model of rank fusions

A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The interaction of users with search services has been recognized as an important mechanism for expressing and handling user information needs. One traditional approach for supporting such interactive search relies on exploiting relevance feedbacks (RF) in the searching process. For large-scale multimedia collections, however, the user efforts required in RF search sessions is considerable. In this paper, we address this issue by proposing a novel semi-supervised approach for implementing RF-based search services. In our approach, supervised learning is performed taking advantage of relevance labels provided by users. Later, an unsupervised learning step is performed with the objective of extracting useful information from the intrinsic dataset structure. Furthermore, our hybrid learning approach considers feedbacks of different users, in collaborative image retrieval (CIR) scenarios. In these scenarios, the relationships among the feedbacks provided by different users are exploited, further reducing the collective efforts. Conducted experiments involving shape, color, and texture datasets demonstrate the effectiveness of the proposed approach. Similar results are also observed in experiments considering multimodal image retrieval tasks.The interaction of users with search services has been recognized as an important mechanism for expressing and handling user information needs. One traditional approach for supporting such interactive search relies on exploiting relevance feedbacks (RF) i2015FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)FAPESP [2013/08645-0, 2013/50169-1]CNPq [306580/2012-8, 484254/2012-0]2013/08645-0; 2013/50169-1306580/2012-8;484254/2012-0SEM INFORMAÇÃ

Efficient Rank-Based Diffusion Process with Assured Convergence

Visual features and representation learning strategies experienced huge advances in the previous decade, mainly supported by deep learning approaches. However, retrieval tasks are still performed mainly based on traditional pairwise dissimilarity measures, while the learned representations lie on high dimensional manifolds. With the aim of going beyond pairwise analysis, post-processing methods have been proposed to replace pairwise measures by globally defined measures, capable of analyzing collections in terms of the underlying data manifold. The most representative approaches are diffusion and ranked-based methods. While the diffusion approaches can be computationally expensive, the rank-based methods lack theoretical background. In this paper, we propose an efficient Rank-based Diffusion Process which combines both approaches and avoids the drawbacks of each one. The obtained method is capable of efficiently approximating a diffusion process by exploiting rank-based information, while assuring its convergence. The algorithm exhibits very low asymptotic complexity and can be computed regionally, being suitable to outside of dataset queries. An experimental evaluation conducted for image retrieval and person re-ID tasks on diverse datasets demonstrates the effectiveness of the proposed approach with results comparable to the state-of-the-art

Explorando informações contextuais para reclassificação de imagens e agregação de listas em tarefas de recuperação de imagens

Orientador: Ricardo da Silva TorresTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Sistemas de Recuperação de Images baseados no Conteúdo (Content-Based Image Retrieval - CBIR) têm como objetivo satisfazer as necessidades dos usuários a partir de especificações de consulta. Dado um padrão de consulta (e.g., uma imagem de consulta) como entrada, um sistema CBIR recupera as imagens mais similares em uma coleção considerando suas propriedades visuais. Como o maior interesse dos usuários diz respeito às primeiras posições da lista de imagens retornadas, a eficácia desses sistemas é extremamente dependente da acurácia da função de distância adotada...Observação: O resumo, na íntegra, poderá ser visualizado no texto completo da tese digitalAbstract: Content-Based Image Retrieval (CBIR) systems aims at meeting the user needs expressed in query specifications. Given a query pattern (e.g., query image) as input, a CBIR system retrieves the most similar images in a collection by taking into account image visual properties. Since users are interested in the images placed at the first positions of the returned ranked lists, accurately ranking collection images is of great relevance...Note: The complete abstract is available with the full electronic documentDoutoradoCiência da ComputaçãoDoutor em Ciência da Computaçã

A platform of recommendation services for digital libraries

Orientador: Ricardo da Silva TorresDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Em virtude do crescimento acelerado de conteúdo nas mais diversas aplicações de bibliotecas digitais, a tarefa de localizar objetos digitais de interesse é cada vez mais desafiadora. Sob essa perspectiva, técnicas de recomendação procuram prover, de acordo com as preferências do usuário final, alternativas de escolha de objetos mantidos em uma biblioteca digital. Essa dissertação concentra-se em aspectos relacionados às técnicas de recomendação e suas interações com aplicações de bibliotecas digitais. Uma plataforma de serviços de recomendação, chamada RecS-DL, é proposta, visando ampliar as possibilidades de utilização das ferramentas de recomendação. A Plataforma RecS-DL apresentada é independente de domínio de aplicação, de tecnologias e técnicas de recomendação. O serviço de recomendação oferecido pode ser facilmente agregado a bibliotecas digitais clientes, assim como novos mecanismos de recomendação podem ser acoplados à plataforma de maneira dinâmica. Este trabalho também apresenta uma especificação formal da plataforma de serviços de recomendação proposta a partir do Arcabouço 5S. Para isso foram propostas novas definições e extensões de conceitos deste arcabouço. Por fim, são apresentados os resultados obtidos a partir de testes realizados com a plataforma. Experimentos foram conduzidos considerando bibliotecas digitais reais e avaliações por potenciais usuários. Resultados experimentais ratificam a hipótese de que a plataforma facilita a interoperabilidade de ferramentas de recomendação em bibliotecas digitaisAbstract: The increasing amount of data in the most diverse digital libraries applications makes the process of finding relevant digital objects a challenging task. From this perspective, recommendation techniques can provide, according to user preferences, relevant digital objects stored in a digital library.This dissertation focuses on recommendation techniques and their interactions with digital libraries applications. A platform for recommendation services, called RecS-DL, has been proposed to support the use of recommendation tools. The proposed RecS-DL Platform is independent of application domain, technology, and recommendation techniques. The recommendation services offered by the platform can be easily incorporated into digital libraries systems. Furthermore, new recommendation engines can also be plugged into the platform in a dynamic way. This work also presents a formal specification of the proposed platform, using the 5S Framework. To do this, new definitions and extensions of this framework are proposed. Finally, we present the results obtained from tests performed with the platform. Experiments were conducted considering real digital libraries and evaluations made by potential users. Experimental results confirm that the platform facilitates the interoperability of recommendation tools in digital libraries systemsMestradoSistemas de InformaçãoMestre em Ciência da Computaçã

A correlation graph approach for unsupervised manifold learning in image retrieval tasks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Effectively measuring the similarity among images is a challenging problem in image retrieval tasks due to the difficulty of considering the dataset manifold. This paper presents an unsupervised manifold learning algorithm that takes into account the intrinsic dataset geometry for defining a more effective distance among images. The dataset structure is modeled in terms of a Correlation Graph (CG) and analyzed using Strongly Connected Components (SCCs). While the Correlation Graph adjacency provides a precise but strict similarity relationship, the Strongly Connected Components analysis expands these relationships considering the dataset geometry. A large and rigorous experimental evaluation protocol was conducted for different image retrieval tasks. The experiments were conducted in different datasets involving various image descriptors. Results demonstrate that the manifold learning algorithm can significantly improve the effectiveness of image retrieval systems. The presented approach yields better results in terms of effectiveness than various methods recently proposed in the literature. (C) 2016 Elsevier B.V. All rights reserved.Effectively measuring the similarity among images is a challenging problem in image retrieval tasks due to the difficulty of considering the dataset manifold. This paper presents an unsupervised manifold learning algorithm that takes into account the intr2086679FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)2013/08645-0; 2013/50169-1306580/2012-8; 484254/2012-0SEM INFORMAÇÃ

Efficient Rank-Based Diffusion Process with Assured Convergence

Visual features and representation learning strategies experienced huge advances in the previous decade, mainly supported by deep learning approaches. However, retrieval tasks are still performed mainly based on traditional pairwise dissimilarity measures, while the learned representations lie on high dimensional manifolds. With the aim of going beyond pairwise analysis, post-processing methods have been proposed to replace pairwise measures by globally defined measures, capable of analyzing collections in terms of the underlying data manifold. The most representative approaches are diffusion and ranked-based methods. While the diffusion approaches can be computationally expensive, the rank-based methods lack theoretical background. In this paper, we propose an efficient Rank-based Diffusion Process which combines both approaches and avoids the drawbacks of each one. The obtained method is capable of efficiently approximating a diffusion process by exploiting rank-based information, while assuring its convergence. The algorithm exhibits very low asymptotic complexity and can be computed regionally, being suitable to outside of dataset queries. An experimental evaluation conducted for image retrieval and person re-ID tasks on diverse datasets demonstrates the effectiveness of the proposed approach with results comparable to the state-of-the-art