33 research outputs found
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
Multimedia geocoding: the RECOD 2014 approach
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)This work describes the approach proposed by the RECOD team for the Placing Task of MediaEval 2014. This task requires the definition of automatic schemes to assign geographical locations to images and videos. Our approach is based on the use of as much evidences as possible (textual, visual, and/or audio descriptors) to geocode a given image/video. We estimate the location of test items by clustering the geographic coordinates of top-ranked items in one or more ranked lists defined in terms of different criteria.This work describes the approach proposed by the RECOD team for the Placing Task of MediaEval 2014. This task requires the definition of automatic schemes to assign geographical locations to images and videos. Our approach is based on the use of as much e1263FAPESP - 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)2013/08645-0 ; 2013/11359-0306580/2012-8 ; 484254/2012-0sem informaçãoMediaEval 2014 Worksho
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
Combining Re-ranking And Rank Aggregation Methods For Image Retrieval
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)This paper presents novel approaches for combining re-ranking and rank aggregation methods aiming at improving the effectiveness of Content-Based Image Retrieval (CBIR) systems. Given a query image as input, CBIR systems retrieve the most similar images in a collection by taking into account image visual properties. In this scenario, accurately ranking collection images is of great relevance. Aiming at improving the effectiveness of CBIR systems, re-ranking and rank aggregation algorithms have been proposed. However, different re-ranking and rank aggregation approaches, applied to different image descriptors, may produce different and complementary image rankings. In this paper, we present four novel approaches for combining these rankings aiming at obtaining more effective results. Several experiments were conducted involving shape, color, and texture descriptors. The proposed approaches are also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate that our approaches can improve significantly the effectiveness of image retrieval systems.751591219144Sao Paulo Research Foundation - FAPESP [2013/08645-0]CNPq [306580/2012-8, 484254/2012-0]CAPESAMDMicrosoft ResearchFundação 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
A BFS-Tree of ranking references for unsupervised manifold learning
Contextual information, defined in terms of the proximity of feature vectors in a feature space, has been successfully used in the construction of search services. These search systems aim to exploit such information to effectively improve ranking results, by taking into account the manifold distribution of features usually encoded. In this paper, a novel unsupervised manifold learning is proposed through a similarity representation based on ranking references. A breadth-first tree is used to represent similarity information given by ranking references and is exploited to discovery underlying similarity relationships. As a result, a more effective similarity measure is computed, which leads to more relevant objects in the returned ranked lists of search sessions. Several experiments conducted on eight public datasets, commonly used for image retrieval benchmarking, demonstrated that the proposed method achieves very high effectiveness results, which are comparable or superior to the ones produced by state-of-the-art approaches
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