Recuperação multimodal e interativa de informação orientada por diversidade

Orientador: Ricardo da Silva TorresTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Os métodos de Recuperação da Informação, especialmente considerando-se dados multimídia, evoluíram para a integração de múltiplas fontes de evidência na análise de relevância de itens em uma tarefa de busca. Neste contexto, para atenuar a distância semântica entre as propriedades de baixo nível extraídas do conteúdo dos objetos digitais e os conceitos semânticos de alto nível (objetos, categorias, etc.) e tornar estes sistemas adaptativos às diferentes necessidades dos usuários, modelos interativos que consideram o usuário mais próximo do processo de recuperação têm sido propostos, permitindo a sua interação com o sistema, principalmente por meio da realimentação de relevância implícita ou explícita. Analogamente, a promoção de diversidade surgiu como uma alternativa para lidar com consultas ambíguas ou incompletas. Adicionalmente, muitos trabalhos têm tratado a ideia de minimização do esforço requerido do usuário em fornecer julgamentos de relevância, à medida que mantém níveis aceitáveis de eficácia. Esta tese aborda, propõe e analisa experimentalmente métodos de recuperação da informação interativos e multimodais orientados por diversidade. Este trabalho aborda de forma abrangente a literatura acerca da recuperação interativa da informação e discute sobre os avanços recentes, os grandes desafios de pesquisa e oportunidades promissoras de trabalho. Nós propusemos e avaliamos dois métodos de aprimoramento do balanço entre relevância e diversidade, os quais integram múltiplas informações de imagens, tais como: propriedades visuais, metadados textuais, informação geográfica e descritores de credibilidade dos usuários. Por sua vez, como integração de técnicas de recuperação interativa e de promoção de diversidade, visando maximizar a cobertura de múltiplas interpretações/aspectos de busca e acelerar a transferência de informação entre o usuário e o sistema, nós propusemos e avaliamos um método multimodal de aprendizado para ranqueamento utilizando realimentação de relevância sobre resultados diversificados. Nossa análise experimental mostra que o uso conjunto de múltiplas fontes de informação teve impacto positivo nos algoritmos de balanceamento entre relevância e diversidade. Estes resultados sugerem que a integração de filtragem e re-ranqueamento multimodais é eficaz para o aumento da relevância dos resultados e também como mecanismo de potencialização dos métodos de diversificação. Além disso, com uma análise experimental minuciosa, nós investigamos várias questões de pesquisa relacionadas à possibilidade de aumento da diversidade dos resultados e a manutenção ou até mesmo melhoria da sua relevância em sessões interativas. Adicionalmente, nós analisamos como o esforço em diversificar afeta os resultados gerais de uma sessão de busca e como diferentes abordagens de diversificação se comportam para diferentes modalidades de dados. Analisando a eficácia geral e também em cada iteração de realimentação de relevância, nós mostramos que introduzir diversidade nos resultados pode prejudicar resultados iniciais, enquanto que aumenta significativamente a eficácia geral em uma sessão de busca, considerando-se não apenas a relevância e diversidade geral, mas também o quão cedo o usuário é exposto ao mesmo montante de itens relevantes e nível de diversidadeAbstract: Information retrieval methods, especially considering multimedia data, have evolved towards the integration of multiple sources of evidence in the analysis of the relevance of items considering a given user search task. In this context, for attenuating the semantic gap between low-level features extracted from the content of the digital objects and high-level semantic concepts (objects, categories, etc.) and making the systems adaptive to different user needs, interactive models have brought the user closer to the retrieval loop allowing user-system interaction mainly through implicit or explicit relevance feedback. Analogously, diversity promotion has emerged as an alternative for tackling ambiguous or underspecified queries. Additionally, several works have addressed the issue of minimizing the required user effort on providing relevance assessments while keeping an acceptable overall effectiveness. This thesis discusses, proposes, and experimentally analyzes multimodal and interactive diversity-oriented information retrieval methods. This work, comprehensively covers the interactive information retrieval literature and also discusses about recent advances, the great research challenges, and promising research opportunities. We have proposed and evaluated two relevance-diversity trade-off enhancement work-flows, which integrate multiple information from images, such as: visual features, textual metadata, geographic information, and user credibility descriptors. In turn, as an integration of interactive retrieval and diversity promotion techniques, for maximizing the coverage of multiple query interpretations/aspects and speeding up the information transfer between the user and the system, we have proposed and evaluated a multimodal learning-to-rank method trained with relevance feedback over diversified results. Our experimental analysis shows that the joint usage of multiple information sources positively impacted the relevance-diversity balancing algorithms. Our results also suggest that the integration of multimodal-relevance-based filtering and reranking was effective on improving result relevance and also boosted diversity promotion methods. Beyond it, with a thorough experimental analysis we have investigated several research questions related to the possibility of improving result diversity and keeping or even improving relevance in interactive search sessions. Moreover, we analyze how much the diversification effort affects overall search session results and how different diversification approaches behave for the different data modalities. By analyzing the overall and per feedback iteration effectiveness, we show that introducing diversity may harm initial results whereas it significantly enhances the overall session effectiveness not only considering the relevance and diversity, but also how early the user is exposed to the same amount of relevant items and diversityDoutoradoCiência da ComputaçãoDoutor em Ciência da ComputaçãoP-4388/2010140977/2012-0CAPESCNP

Semantic multimedia analysis using knowledge and context

PhDThe difficulty of semantic multimedia analysis can be attributed to the extended diversity in form and appearance exhibited by the majority of semantic concepts and the difficulty to express them using a finite number of patterns. In meeting this challenge there has been a scientific debate on whether the problem should be addressed from the perspective of using overwhelming amounts of training data to capture all possible instantiations of a concept, or from the perspective of using explicit knowledge about the concepts’ relations to infer their presence. In this thesis we address three problems of pattern recognition and propose solutions that combine the knowledge extracted implicitly from training data with the knowledge provided explicitly in structured form. First, we propose a BNs modeling approach that defines a conceptual space where both domain related evi- dence and evidence derived from content analysis can be jointly considered to support or disprove a hypothesis. The use of this space leads to sig- nificant gains in performance compared to analysis methods that can not handle combined knowledge. Then, we present an unsupervised method that exploits the collective nature of social media to automatically obtain large amounts of annotated image regions. By proving that the quality of the obtained samples can be almost as good as manually annotated images when working with large datasets, we significantly contribute towards scal- able object detection. Finally, we introduce a method that treats images, visual features and tags as the three observable variables of an aspect model and extracts a set of latent topics that incorporates the semantics of both visual and tag information space. By showing that the cross-modal depen- dencies of tagged images can be exploited to increase the semantic capacity of the resulting space, we advocate the use of all existing information facets in the semantic analysis of social media

Large-scale image collection cleansing, summarization and exploration

A perennially interesting topic in the research field of large scale image collection organization is how to effectively and efficiently conduct the tasks of image cleansing, summarization and exploration. The primary objective of such an image organization system is to enhance user exploration experience with redundancy removal and summarization operations on large-scale image collection. An ideal system is to discover and utilize the visual correlation among the images, to reduce the redundancy in large-scale image collection, to organize and visualize the structure of large-scale image collection, and to facilitate exploration and knowledge discovery. In this dissertation, a novel system is developed for exploiting and navigating large-scale image collection. Our system consists of the following key components: (a) junk image filtering by incorporating bilingual search results; (b) near duplicate image detection by using a coarse-to-fine framework; (c) concept network generation and visualization; (d) image collection summarization via dictionary learning for sparse representation; and (e) a multimedia practice of graffiti image retrieval and exploration. For junk image filtering, bilingual image search results, which are adopted for the same keyword-based query, are integrated to automatically identify the clusters for the junk images and the clusters for the relevant images. Within relevant image clusters, the results are further refined by removing the duplications under a coarse-to-fine structure. The duplicate pairs are detected with both global feature (partition based color histogram) and local feature (CPAM and SIFT Bag-of-Word model). The duplications are detected and removed from the data collection to facilitate further exploration and visual correlation analysis. After junk image filtering and duplication removal, the visual concepts are further organized and visualized by the proposed concept network. An automatic algorithm is developed to generate such visual concept network which characterizes the visual correlation between image concept pairs. Multiple kernels are combined and a kernel canonical correlation analysis algorithm is used to characterize the diverse visual similarity contexts between the image concepts. The FishEye visualization technique is implemented to facilitate the navigation of image concepts through our image concept network. To better assist the exploration of large scale data collection, we design an efficient summarization algorithm to extract representative examplars. For this collection summarization task, a sparse dictionary (a small set of the most representative images) is learned to represent all the images in the given set, e.g., such sparse dictionary is treated as the summary for the given image set. The simulated annealing algorithm is adopted to learn such sparse dictionary (image summary) by minimizing an explicit optimization function. In order to handle large scale image collection, we have evaluated both the accuracy performance of the proposed algorithms and their computation efficiency. For each of the above tasks, we have conducted experiments on multiple public available image collections, such as ImageNet, NUS-WIDE, LabelMe, etc. We have observed very promising results compared to existing frameworks. The computation performance is also satisfiable for large-scale image collection applications. The original intention to design such a large-scale image collection exploration and organization system is to better service the tasks of information retrieval and knowledge discovery. For this purpose, we utilize the proposed system to a graffiti retrieval and exploration application and receive positive feedback

From Word to Sense Embeddings: A Survey on Vector Representations of Meaning

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence Researc

Semantic multimedia modelling & interpretation for annotation

The emergence of multimedia enabled devices, particularly the incorporation of cameras in mobile phones, and the accelerated revolutions in the low cost storage devices, boosts the multimedia data production rate drastically. Witnessing such an iniquitousness of digital images and videos, the research community has been projecting the issue of its significant utilization and management. Stored in monumental multimedia corpora, digital data need to be retrieved and organized in an intelligent way, leaning on the rich semantics involved. The utilization of these image and video collections demands proficient image and video annotation and retrieval techniques. Recently, the multimedia research community is progressively veering its emphasis to the personalization of these media. The main impediment in the image and video analysis is the semantic gap, which is the discrepancy among a user’s high-level interpretation of an image and the video and the low level computational interpretation of it. Content-based image and video annotation systems are remarkably susceptible to the semantic gap due to their reliance on low-level visual features for delineating semantically rich image and video contents. However, the fact is that the visual similarity is not semantic similarity, so there is a demand to break through this dilemma through an alternative way. The semantic gap can be narrowed by counting high-level and user-generated information in the annotation. High-level descriptions of images and or videos are more proficient of capturing the semantic meaning of multimedia content, but it is not always applicable to collect this information. It is commonly agreed that the problem of high level semantic annotation of multimedia is still far from being answered. This dissertation puts forward approaches for intelligent multimedia semantic extraction for high level annotation. This dissertation intends to bridge the gap between the visual features and semantics. It proposes a framework for annotation enhancement and refinement for the object/concept annotated images and videos datasets. The entire theme is to first purify the datasets from noisy keyword and then expand the concepts lexically and commonsensical to fill the vocabulary and lexical gap to achieve high level semantics for the corpus. This dissertation also explored a novel approach for high level semantic (HLS) propagation through the images corpora. The HLS propagation takes the advantages of the semantic intensity (SI), which is the concept dominancy factor in the image and annotation based semantic similarity of the images. As we are aware of the fact that the image is the combination of various concepts and among the list of concepts some of them are more dominant then the other, while semantic similarity of the images are based on the SI and concept semantic similarity among the pair of images. Moreover, the HLS exploits the clustering techniques to group similar images, where a single effort of the human experts to assign high level semantic to a randomly selected image and propagate to other images through clustering. The investigation has been made on the LabelMe image and LabelMe video dataset. Experiments exhibit that the proposed approaches perform a noticeable improvement towards bridging the semantic gap and reveal that our proposed system outperforms the traditional systems

A Survey on Visual Analytics of Social Media Data

The unprecedented availability of social media data offers substantial opportunities for data owners, system operators, solution providers, and end users to explore and understand social dynamics. However, the exponential growth in the volume, velocity, and variability of social media data prevents people from fully utilizing such data. Visual analytics, which is an emerging research direction, ha..