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

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

Multimedia retrieval through unsupervised hypergraph-based manifold ranking

Accurately ranking images and multimedia objects are of paramount relevance in many retrieval and learning tasks. Manifold learning methods have been investigated for ranking mainly due to their capacity of taking into account the intrinsic global manifold structure. In this paper, a novel manifold ranking algorithm is proposed based on the hypergraphs for unsupervised multimedia retrieval tasks. Different from traditional graph-based approaches, which represent only pairwise relationships, hypergraphs are capable of modeling similarity relationships among a set of objects. The proposed approach uses the hyperedges for constructing a contextual representation of data samples and exploits the encoded information for deriving a more effective similarity function. An extensive experimental evaluation was conducted on nine public datasets including diverse retrieval scenarios and multimedia content. Experimental results demonstrate that high effectiveness gains can be obtained in comparison with the state-of-the-art methods281258245838CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP423228/2016-1; 307560/2016-3; 308194/2017-9; 313122/2017-2Sem informação2018/15597-6; 2017/25908-6; 2017/02091-4; 2017/20945-0; 2016/06441-7; 2015/24494-8; 2016/50250-1; 2013/50155-0; 2014/12236-1; 2014/50715-

A Unified Model for Accelerating Unsupervised Iterative Re-Ranking Algorithms

Despite the continuous advances in image retrieval technologies, performing effective and efficient content-based searches remains a challenging task. Unsupervised iterative re-ranking algorithms have emerged as a promising solution and have been widely used to improve the effectiveness of multimedia retrieval systems. Although substantially more efficient than related approaches based on diffusion processes, these re-ranking algorithms can still be computationally costly, demanding the specification and implementation of efficient big multimedia analysis approaches. Such demand associated with the significant potential for parallelization and highly effective results achieved by recently proposed re-ranking algorithms creates the need for exploiting efficiency vs effectiveness trade-offs. In this article, we introduce a class of unsupervised iterative re-ranking algorithms and present a model that can be used to guide their implementation and optimization for parallel architectures. We also analyze the impact of the parallelization on the performance of four algorithms that belong to the proposed class: Contextual Spaces, RL-Sim, Contextual Re-ranking, and Cartesian Product of Ranking References. The experiments show speedups that reach up to 6.0×,16.1×,3.3×, and7.1×for each algorithm, respectively. These results demonstrate that the proposed parallel programming model can be successfully applied to various algorithms and used to improve the performance of multimedia retrieval systems

A unified model for accelerating unsupervised iterative re‐ranking algorithms

Despite the continuous advances in image retrieval technologies, performing effective and efficient content‐based searches remains a challenging task. Unsupervised iterative re‐ranking algorithms have emerged as a promising solution and have been widely used to improve the effectiveness of multimedia retrieval systems. Although substantially more efficient than related approaches based on diffusion processes, these re‐ranking algorithms can still be computationally costly, demanding the specification and implementation of efficient big multimedia analysis approaches. Such demand associated with the significant potential for parallelization and highly effective results achieved by recently proposed re‐ranking algorithms creates the need for exploiting efficiency vs effectiveness trade‐offs. In this article, we introduce a class of unsupervised iterative re‐ranking algorithms and present a model that can be used to guide their implementation and optimization for parallel architectures. We also analyze the impact of the parallelization on the performance of four algorithms that belong to the proposed class: Contextual Spaces, RL‐Sim, Contextual Re‐ranking, and Cartesian Product of Ranking References. The experiments show speedups that reach up to 6.0×, 16.1×, 3.3×, and 7.1× for each algorithm, respectively. These results demonstrate that the proposed parallel programming model can be successfully applied to various algorithms and used to improve the performance of multimedia retrieval systems3214CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP307560/2016‐3; 484254/2012‐0; 308194/2017‐9; 140653/2017‐188881.145912/2017‐012018/15597‐6; 2017/25908‐6; 2014/12236‐1; 2015/24494‐8; 2016/50250‐1; 2017/20945‐0; 2019/19312‐9; 2013/50155‐0; 2013/50169‐1; 2014/50715‐9The authors thank AMD, FAEPEX, CAPES (grant #88881.145912/2017‐01), FAPESP (grants #2018/15597‐6, 2017/25908‐6, #2014/12236‐1, #2015/24494‐8, #2016/50250‐1, #2017/20945‐0, and #2019/19312‐9), the FAPESP‐Microsoft Virtual Institute (grants #2013/50155‐0, #2013/50169‐1, and #2014/50715‐9), and CNPq (grants #307560/2016‐3, #484254/2012‐0, #308194/2017‐9, and #140653/2017‐1) for the financial suppor