Multimodal non-linear latent semantic method for information retrieval

La búsqueda y recuperación de datos multimodales es una importante tarea dentro del campo de búsqueda y recuperación de información, donde las consultas y los elementos de la base de datos objetivo están representados por un conjunto de modalidades, donde cada una de ellas captura un aspecto de un fenómeno de interés. Cada modalidad contiene información complementaria y común a otras modalidades. Con el fin de tomar ventaja de la información adicional distribuida a través de las distintas modalidades han sido desarrollados muchos algoritmos y métodos que utilizan las propiedades estadísticas en los datos multimodales para encontrar correlaciones implícitas, otros aprenden a calcular distancias heterogéneas, otros métodos aprenden a proyectar los datos desde el espacio de entrada hasta un espacio semántico común, donde las diferentes modalidades son comparables y se puede construir un ranking a partir de ellas. En esta tesis se presenta el diseño de un sistema para la búsqueda y recuperación de información multimodal que aprende varias proyecciones no lineales a espacios semánticos latentes donde las distintas modalidades son representadas en conjunto y es posible realizar comparaciones y medidas de similitud para construir rankings multimodales. Adicionalmente se propone un método kernelizado para la proyección de datos a un espacio semántico latente usando la información de las etiquetas como método de supervisión para construir índice multimodal que integra los datos multimodales y la información de las etiquetas; este método puede proyectar los datos a tres diferentes espacios semánticos donde varias configuraciones de búsqueda y recuperación de información pueden ser aplicadas. El sistema y el método propuestos fueron evaluados en un conjunto de datos compuesto por casos médicos, donde cada caso consta de una imagen de tejido prostático, un reporte de texto del patólogo y un valor de Gleason score como etiqueta de supervisión. Combinando la información multimodal y la información en las etiquetas se generó un índice multimodal que se utilizó para realizar la tarea de búsqueda y recuperación de información por contenido obteniendo resultados sobresalientes. Las proyecciones no-lineales permiten al modelo una mayor flexibilidad y capacidad de representación. Sin embargo calcular estas proyecciones no-lineales en un conjunto de datos enorme es computacionalmente costoso, para reducir este costo y habilitar el modelo para procesar datos a gran escala, la técnica del budget fue utilizada, mostrando un buen compromiso entre efectividad y velocidad.Multimodal information retrieval is an information retrieval sub-task where queries and database target elements are composed of several modalities or views. A modality is a representation of complex phenomena, captured and measured by different sensors or information sources, each one encodes some information about it. Each modality representation contains complementary and shared information about the phenomenon of interest, this additional information can be used to improve the information retrieval process. Several methods have been developed to take advantage of additional information distributed across different modalities. Some of them exploit statistical properties in multimodal data to find correlations and implicit relationships, others learn heterogeneous distance functions, and others learn linear and non-linear projections that transform data from the original input space to a common latent semantic space where different modalities are comparable. In spite of the attention dedicated to this issue, multimodal information retrieval is still an open problem. This thesis presents a multimodal information retrieval system designed to learn several mapping functions to transform multimodal data to a latent semantic space, where different modalities are combined and can be compared to build a multimodal ranking and perform a multimodal information retrieval task. Additionally, a multimodal kernelized latent semantic embedding method is proposed to construct a supervised multimodal index, integrating multimodal data and label supervision. This method can perform mappings to three different spaces where some information retrieval task setups can be performed. The proposed system and method were evaluated in a multimodal medical case-based retrieval task where data is composed of whole-slide images of prostate tissue samples, pathologist’s text report and Gleason score as a supervised label. Multimodal data and labels were combined to produce a multimodal index. This index was used to retrieve multimodal information and achieves outstanding results compared with previous works on this topic. Non-linear mappings provide more flexibility and representation capacity to the proposed model. However, constructing the non-linear mapping in a large dataset using kernel methods can be computationally costly. To reduce the cost and allow large scale applications, the budget technique was introduced, showing good performance between speed and effectiveness.COLCIENCIASJóvenes investigadores 761/2016Línea de investigación: Ciencias de la computaciónMaestrí

Image Recommendation Based on Keyword Relevance Using Absorbing Markov Chain and Image Features

Image recommendation is an important feature of search engine, as tremendous amount of images are available online. It is necessary to retrieve relevant images to meet the user's requirement. In this paper, we present an algorithm image recommendation with absorbing Markov chain (IRAbMC) to retrieve relevant images for a user's input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Keyword relevance is computed using absorbing Markov chain. Images are reranked using image visual features. Experimental results show that the IRAbMC algorithm outperforms Markovian semantic indexing (MSI) method with improved relevance score of retrieved ranked images

From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field

Machine Learning for Information Retrieval

In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. Thisidentification is performed through a ranking task. Given a user query, an ad-hoc retrieval system ranks the corpus documents, so that the documents relevant to the query ideally appear above the others. In a machine learning framework, we are interested in proposing learning algorithms that can benefit from limited training data in order to identify a ranker likely to achieve high retrieval performance over unseen documents and queries. This problem presents novel challenges compared to traditional learning tasks, such as regression or classification. First, our task is a ranking problem, which means that the loss for a given query cannot be measured as a sum of an individual loss suffered for each corpus document. Second, most retrieval queries present a highly unbalanced setup, with a set of relevant documents accounting only for a very small fraction of the corpus. Third, ad-hoc retrieval corresponds to a kind of ``double'' generalization problem, since the learned model should not only generalize to new documents but also to new queries. Finally, our task also presents challenging efficiency constraints, since ad-hoc retrieval is typically applied to large corpora. % The main objective of this thesis is to investigate the discriminative learning of ad-hoc retrieval models. For that purpose, we propose different models based on kernel machines or neural networks adapted to different retrieval contexts. The proposed approaches rely on different online learning algorithms that allow efficient learning over large corpora. The first part of the thesis focus on text retrieval. In this case, we adopt a classical approach to the retrieval ranking problem, and order the text documents according to their estimated similarity to the text query. The assessment of semantic similarity between text items plays a key role in that setup and we propose a learning approach to identify an effective measure of text similarity. This identification is not performed relying on a set of queries with their corresponding relevant document sets, since such data are especially expensive to label and hence rare. Instead, we propose to rely on hyperlink data, since hyperlinks convey semantic proximity information that is relevant to similarity learning. This setup is hence a transfer learning setup, where we benefit from the proximity information encoded by hyperlinks to improve the performance over the ad-hoc retrieval task. We then investigate another retrieval problem, i.e. the retrieval of images from text queries. Our approach introduces a learning procedure optimizing a criterion related to the ranking performance. This criterion adapts our previous learning objective for learning textual similarity to the image retrieval problem. This yields an image ranking model that addresses the retrieval problem directly. This approach contrasts with previous research that rely on an intermediate image annotation task. Moreover, our learning procedure builds upon recent work on the online learning of kernel-based classifiers. This yields an efficient, scalable algorithm, which can benefit from recent kernels developed for image comparison. In the last part of the thesis, we show that the objective function used in the previous retrieval problems can be applied to the task of keyword spotting, i.e. the detection of given keywords in speech utterances. For that purpose, we formalize this problem as a ranking task: given a keyword, the keyword spotter should order the utterances so that the utterances containing the keyword appear above the others. Interestingly, this formulation yields an objective directly maximizing the area under the receiver operating curve, the most common keyword spotter evaluation measure. This objective is then used to train a model adapted to this intrinsically sequential problem. This model is then learned with a procedure derived from the algorithm previously introduced for the image retrieval task. To conclude, this thesis introduces machine learning approaches for ad-hoc retrieval. We propose learning models for various multi-modal retrieval setups, i.e. the retrieval of text documents from text queries, the retrieval of images from text queries and the retrieval of speech recordings from written keywords. Our approaches rely on discriminative learning and enjoy efficient training procedures, which yields effective and scalable models. In all cases, links with prior approaches were investigated and experimental comparisons were conducted

Information Retrieval: Recent Advances and Beyond

In this paper, we provide a detailed overview of the models used for information retrieval in the first and second stages of the typical processing chain. We discuss the current state-of-the-art models, including methods based on terms, semantic retrieval, and neural. Additionally, we delve into the key topics related to the learning process of these models. This way, this survey offers a comprehensive understanding of the field and is of interest for for researchers and practitioners entering/working in the information retrieval domain

COST292 experimental framework for TRECVID 2008

In this paper, we give an overview of the four tasks submitted to TRECVID 2008 by COST292. The high-level feature extraction framework comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a multi-modal classifier based on SVMs and several descriptors. The third system uses three image classifiers based on ant colony optimisation, particle swarm optimisation and a multi-objective learning algorithm. The fourth system uses a Gaussian model for singing detection and a person detection algorithm. The search task is based on an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. The rushes task submission is based on a spectral clustering approach for removing similar scenes based on eigenvalues of frame similarity matrix and and a redundancy removal strategy which depends on semantic features extraction such as camera motion and faces. Finally, the submission to the copy detection task is conducted by two different systems. The first system consists of a video module and an audio module. The second system is based on mid-level features that are related to the temporal structure of videos