Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation

Remote sensing (RS) image retrieval is of great significant for geological information mining. Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues: feature extraction, similarity metric and relevance feedback. Due to the complexity and multiformity of ground objects in high-resolution remote sensing (HRRS) images, there is still room for improvement in the current retrieval approaches. In this paper, we analyze the three core issues of RS image retrieval and provide a comprehensive review on existing methods. Furthermore, for the goal to advance the state-of-the-art in HRRS image retrieval, we focus on the feature extraction issue and delve how to use powerful deep representations to address this task. We conduct systematic investigation on evaluating correlative factors that may affect the performance of deep features. By optimizing each factor, we acquire remarkable retrieval results on publicly available HRRS datasets. Finally, we explain the experimental phenomenon in detail and draw conclusions according to our analysis. Our work can serve as a guiding role for the research of content-based RS image retrieval

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

Agregação de ranks baseada em grafos

Orientador: Ricardo da Silva TorresTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Neste trabalho, apresentamos uma abordagem robusta de agregação de listas baseada em grafos, capaz de combinar resultados de modelos de recuperação isolados. O método segue um esquema não supervisionado, que é independente de como as listas isoladas são geradas. Nossa abordagem é capaz de incorporar modelos heterogêneos, de diferentes critérios de recuperação, tal como baseados em conteúdo textual, de imagem ou híbridos. Reformulamos o problema de recuperação ad-hoc como uma recuperação baseada em fusion graphs, que propomos como um novo modelo de representação unificada capaz de mesclar várias listas e expressar automaticamente inter-relações de resultados de recuperação. Assim, mostramos que o sistema de recuperação se beneficia do aprendizado da estrutura intrínseca das coleções, levando a melhores resultados de busca. Nossa formulação de agregação baseada em grafos, diferentemente das abordagens existentes, permite encapsular informação contextual oriunda de múltiplas listas, que podem ser usadas diretamente para ranqueamento. Experimentos realizados demonstram que o método apresenta alto desempenho, produzindo melhores eficácias que métodos recentes da literatura e promovendo ganhos expressivos sobre os métodos de recuperação fundidos. Outra contribuição é a extensão da proposta de grafo de fusão visando consulta eficiente. Trabalhos anteriores são promissores quanto à eficácia, mas geralmente ignoram questões de eficiência. Propomos uma função inovadora de agregação de consulta, não supervisionada, intrinsecamente multimodal almejando recuperação eficiente e eficaz. Introduzimos os conceitos de projeção e indexação de modelos de representação de agregação de consulta com base em grafos, e a sua aplicação em tarefas de busca. Formulações de projeção são propostas para representações de consulta baseadas em grafos. Introduzimos os fusion vectors, uma representação de fusão tardia de objetos com base em listas, a partir da qual é definido um modelo de recuperação baseado intrinsecamente em agregação. A seguir, apresentamos uma abordagem para consulta rápida baseada nos vetores de fusão, promovendo agregação de consultas eficiente. O método apresentou alta eficácia quanto ao estado da arte, além de trazer uma perspectiva de eficiência pouco abordada. Ganhos consistentes de eficiência são alcançadas em relação aos trabalhos recentes. Também propomos modelos de representação baseados em consulta para problemas gerais de predição. Os conceitos de grafos de fusão e vetores de fusão são estendidos para cenários de predição, nos quais podem ser usados para construir um modelo de estimador para determinar se um objeto de avaliação (ainda que multimodal) se refere a uma classe ou não. Experimentos em tarefas de classificação multimodal, tal como detecção de inundação, mostraram que a solução é altamente eficaz para diferentes cenários de predição que envolvam dados textuais, visuais e multimodais, produzindo resultados melhores que vários métodos recentes. Por fim, investigamos a adoção de abordagens de aprendizagem para ajudar a otimizar a criação de modelos de representação baseados em consultas, a fim de maximizar seus aspectos de capacidade discriminativa e eficiência em tarefas de predição e de buscaAbstract: In this work, we introduce a robust graph-based rank aggregation approach, capable of combining 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 incorporate heterogeneous 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 graph-based retrieval based on {\em 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 show that the retrieval system can benefit from learning the manifold structure of datasets, thus leading to more effective results. Our graph-based aggregation formulation, unlike existing approaches, allows for encapsulating contextual information encoded from multiple ranks, which can be directly used for ranking. 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. Another contribution refers to the extension of the fusion graph solution for efficient rank aggregation. Although previous works are promising with respect to effectiveness, they usually overlook efficiency aspects. We propose an innovative rank aggregation function that it is unsupervised, intrinsically multimodal, and targeted for fast retrieval and top effectiveness performance. We introduce the concepts of embedding and indexing graph-based rank-aggregation representation models, and their application for search tasks. Embedding formulations are also proposed for graph-based rank representations. We introduce the concept of {\em fusion vectors}, a late-fusion representation of objects based on ranks, from which an intrinsically rank-aggregation retrieval model is defined. Next, we present an approach for fast retrieval based on fusion vectors, thus promoting an efficient rank aggregation system. Our method presents top effectiveness performance among state-of-the-art related work, while promoting an efficiency perspective not yet covered. Consistent speedups are achieved against the recent baselines in all datasets considered. Derived from the fusion graphs and fusion vectors, we propose rank-based representation models for general prediction problems. The concepts of fusion graphs and fusion vectors are extended to prediction scenarios, where they can be used to build an estimator model to determine whether an input (even multimodal) object refers to a class or not. Performed experiments in the context of multimodal classification tasks, such as flood detection, show that the proposed solution is highly effective for different detection scenarios involving textual, visual, and multimodal features, yielding better detection results than several state-of-the-art methods. Finally, we investigate the adoption of learning approaches to help optimize the creation of rank-based representation models, in order to maximize their discriminative power and efficiency aspects in prediction and search tasksDoutoradoCiência da ComputaçãoDoutor em Ciência da Computaçã

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Bridging the semantic gap in content-based image retrieval.

To manage large image databases, Content-Based Image Retrieval (CBIR) emerged as a new research subject. CBIR involves the development of automated methods to use visual features in searching and retrieving. Unfortunately, the performance of most CBIR systems is inherently constrained by the low-level visual features because they cannot adequately express the user\u27s high-level concepts. This is known as the semantic gap problem. This dissertation introduces a new approach to CBIR that attempts to bridge the semantic gap. Our approach includes four components. The first one learns a multi-modal thesaurus that associates low-level visual profiles with high-level keywords. This is accomplished through image segmentation, feature extraction, and clustering of image regions. The second component uses the thesaurus to annotate images in an unsupervised way. This is accomplished through fuzzy membership functions to label new regions based on their proximity to the profiles in the thesaurus. The third component consists of an efficient and effective method for fusing the retrieval results from the multi-modal features. Our method is based on learning and adapting fuzzy membership functions to the distribution of the features\u27 distances and assigning a degree of worthiness to each feature. The fourth component provides the user with the option to perform hybrid querying and query expansion. This allows the enrichment of a visual query with textual data extracted from the automatically labeled images in the database. The four components are integrated into a complete CBIR system that can run in three different and complementary modes. The first mode allows the user to query using an example image. The second mode allows the user to specify positive and/or negative sample regions that should or should not be included in the retrieved images. The third mode uses a Graphical Text Interface to allow the user to browse the database interactively using a combination of low-level features and high-level concepts. The proposed system and ail of its components and modes are implemented and validated using a large data collection for accuracy, performance, and improvement over traditional CBIR techniques

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges