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

Exploiting ConvNet Diversity for Flooding Identification

Flooding is the world's most costly type of natural disaster in terms of both economic losses and human causalities. A first and essential procedure toward flood monitoring is based on identifying the area most vulnerable to flooding, which gives authorities relevant regions to focus. In this letter, we propose several methods to perform flooding identification in high-resolution remote sensing images using deep learning. Specifically, some proposed techniques are based upon unique networks, such as dilated and deconvolutional ones, whereas others were conceived to exploit diversity of distinct networks in order to extract the maximum performance of each classifier. The evaluation of the proposed methods was conducted in a high-resolution remote sensing data set. Results show that the proposed algorithms outperformed the state-of-the-art baselines, providing improvements ranging from 1% to 4% in terms of the Jaccard Index

Semi-supervised learning for relevance feedback on image retrieval tasks

Relevance feedback approaches have been established as an important tool for interactive search, enabling users to express their needs. However, in view of the growth of multimedia collections available, the user efforts required by these methods tend to increase as well, demanding approaches for reducing the need of user interactions. In this context, this paper proposes a semi-supervised learning algorithm for relevance feedback to be used in image retrieval tasks. The proposed semi-supervised algorithm aims at using both supervised and unsupervised approaches simultaneously. While a supervised step is performed using the information collected from the user feedback, an unsupervised step exploits the intrinsic dataset structure, which is represented in terms of ranked lists of images. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors and different datasets. The proposed approach was also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate the effectiveness of the proposed approach

Unsupervised distance learning by reciprocal kNN distance for image retrieval

This paper presents a novel unsupervised learning approach that takes into account the intrinsic dataset structure, which is represented in terms of the reciprocal neighborhood references found in different ranked lists. The proposed Reciprocal kNN Distance defines a more effective distance between two images, and is used to improve the effectiveness of image retrieval systems. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors. The proposed approach is also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate the effectiveness of proposed approach. The Reciprocal kNN Distance yields better results in terms of effectiveness than various state-of-the-art algorithms. Copyright © 2014 ACM.This paper presents a novel unsupervised learning approach that takes into account the intrinsic dataset structure, which is represented in terms of the reciprocal neighborhood references found in different ranked lists. The proposed Reciprocal kNN Distan345352FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR2013/08645-0306580/2012-8 ; 484254/2012-0sem informação4. International Conference on Multimedia Retrieva

Recod @ MediaEval 2014: Diverse social images retrieval

This paper presents the results of the rst participation of our multi-institutional team in the Retrieving Diverse Social Images Task at MediaEval 2014. In this task we were required to develop a summarization and diversi cation approach for social photo retrieval. Our approach is based on irrelevant image ltering, image re-ranking, and diversity promotion by clustering. We have used visual and textual features, including image metadata and user credibility information1263FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP2013/11359-0MediaEval 2014 Multimedia Benchmark Worksho

Characterization of cultivable intestinal microbiota in Rhynchophorus palmarum Linnaeus (Coleoptera: Curculionidae) and determination of its cellulolytic activity

Rhynchophorus palmarum Linnaeus is an agricultural pest that affects various palm crops, including coconut (Cocos nucifera) plantations which are prominent in the economy of Northeastern Brazil. Characterization of the intestinal microbiota of R. palmarum, as well as elucidation of aspects related to the biochemistry and physiology of the insect's digestion, is essential for intervention in specific metabolic processes as a form of pest control. Thus, this study aimed to characterize the intestinal microbiota of R. palmarum and investigate its ability to degrade cellulosic substrates, to explore new biological control measures. Intestinal dissection of eight adult R. palmarum insects was performed in a laminar flow chamber, and the intestines were homogenized in sterile phosphate-buffered saline solution. Subsequently, serial dilution aliquots of these solutions were spread on nutritive agar plates for the isolation of bacteria and fungi. The microorganisms were identified by matrix-assisted laser desorption/ionization with a time-of-flight mass spectrometry and evaluated for their ability to degrade cellulose. Fourteen bacterial genera (Acinetobacter, Alcaligenes, Arthrobacter, Bacillus, Citrobacter, Enterococcus, Kerstersia, Lactococcus, Micrococcus, Proteus, Providencia, Pseudomonas, Serratia, and Staphylococcus) and two fungal genera (Candida and Saccharomyces)—assigned to the Firmicutes, Actinobacteria, Proteobacteria, and Ascomycota phyla—were identified. The cellulolytic activity was exhibited by six bacterial and one fungal species; of these, Bacillus cereus demonstrated the highest enzyme synthesis (enzymatic index = 4.6). This is the first study characterizing the R. palmarum intestinal microbiota, opening new perspectives for the development of strategies for the biological control of this insect.Fil: Nunes Calumby, Rodrigo José. Universidade Federal de Alagoas; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; ArgentinaFil: Almeida, Lara M.. Universidade Federal de Alagoas; BrasilFil: Barros, Yasmin N.. Universidade Federal de Sao Paulo; BrasilFil: Segura, Wilson D.. Universidade Federal de Sao Paulo; BrasilFil: Barbosa, Valcilaine T.. Universidade Federal de Alagoas; BrasilFil: Silva, Antonio T.. Universidade Federal de Alagoas; BrasilFil: Dornelas, Camila B.. Universidade Federal de Alagoas; BrasilFil: Alvino, Valter. Universidade Federal de Alagoas; BrasilFil: Grillo, Luciano A. M.. Universidade Federal de Alagoas; Brasi