Automatic image annotation and object detection

We live in the midst of the information era, during which organising and indexing information more effectively is a matter of essential importance. With the fast development of digital imagery, how to search images - a rich form of information - more efficiently by their content has become one of the biggest challenges. Content-based image retrieval (CBIR) has been the traditional and dominant technique for searching images for decades. However, not until recently have researchers started to realise some vital problems existing in CBIR systems. One of the most important is perhaps what people call the \textit{semantic gap}, which refers to the gap between the information that can be extracted from images and the interpretation of the images for humans. As an attempt to bridge the semantic gap, automatic image annotation has been gaining more and more attentions in recent years. This thesis aims to explore a number of different approaches to automatic image annotation and some related issues. It begins with an introduction into different techniques for image description, which forms the foundation of the research on image auto-annotation. The thesis then goes on to give an in-depth examination of some of the quality issues of the data-set used for evaluating auto-annotation systems. A series of approaches to auto-annotation are presented in the follow-up chapters. Firstly, we describe an approach that incorporates the salient based image representation into a statistical model for better annotation performance. Secondly, we explore the use of non-negative matrix factorisation (NMF), a matrix decomposition tehcnique, for two tasks; object class detection and automatic annotation of images. The results imply that NMF is a promising sub-space technique for these purposes. Finally, we propose a model named the image based feature space (IBFS) model for linking image regions and keywords, and for image auto-annotation. Both image regions and keywords are mapped into the same space in which their relationships can be measured. The idea of multiple segmentations is then implemented in the model, and better results are achieved than using a single segmentation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Semantic spaces revisited: investigating the performance of auto-annotation and semantic retrieval using semantic spaces

Semantic spaces encode similarity relationships between objects as a function of position in a mathematical space. This paper discusses three different formulations for building semantic spaces which allow the automatic-annotation and semantic retrieval of images. The models discussed in this paper require that the image content be described in the form of a series of visual-terms, rather than as a continuous feature-vector. The paper also discusses how these term-based models compare to the latest state-of-the-art continuous feature models for auto-annotation and retrieval

Learning based automatic face annotation for arbitrary poses and expressions from frontal images only

Statistical approaches for building non-rigid deformable models, such as the active appearance model (AAM), have enjoyed great popularity in recent years, but typically require tedious manual annotation of training images. In this paper, a learning based approach for the automatic annotation of visually deformable objects from a single annotated frontal image is presented and demonstrated on the example of automatically annotating face images that can be used for building AAMs for fitting and tracking. This approach employs the idea of initially learning the correspondences between landmarks in a frontal image and a set of training images with a face in arbitrary poses. Using this learner, virtual images of unseen faces at any arbitrary pose for which the learner was trained can be reconstructed by predicting the new landmark locations and warping the texture from the frontal image. View-based AAMs are then built from the virtual images and used for automatically annotating unseen images, including images of different facial expressions, at any random pose within the maximum range spanned by the virtually reconstructed images. The approach is experimentally validated by automatically annotating face images from three different databases

Mind the Gap: Another look at the problem of the semantic gap in image retrieval

This paper attempts to review and characterise the problem of the semantic gap in image retrieval and the attempts being made to bridge it. In particular, we draw from our own experience in user queries, automatic annotation and ontological techniques. The first section of the paper describes a characterisation of the semantic gap as a hierarchy between the raw media and full semantic understanding of the media's content. The second section discusses real users' queries with respect to the semantic gap. The final sections of the paper describe our own experience in attempting to bridge the semantic gap. In particular we discuss our work on auto-annotation and semantic-space models of image retrieval in order to bridge the gap from the bottom up, and the use of ontologies, which capture more semantics than keyword object labels alone, as a technique for bridging the gap from the top down

Fixation prediction with a combined model of bottom-up saliency and vanishing point

By predicting where humans look in natural scenes, we can understand how they perceive complex natural scenes and prioritize information for further high-level visual processing. Several models have been proposed for this purpose, yet there is a gap between best existing saliency models and human performance. While many researchers have developed purely computational models for fixation prediction, less attempts have been made to discover cognitive factors that guide gaze. Here, we study the effect of a particular type of scene structural information, known as the vanishing point, and show that human gaze is attracted to the vanishing point regions. We record eye movements of 10 observers over 532 images, out of which 319 have vanishing points. We then construct a combined model of traditional saliency and a vanishing point channel and show that our model outperforms state of the art saliency models using three scores on our dataset.Comment: arXiv admin note: text overlap with arXiv:1512.0172

Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval.

Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches

Information extraction from multimedia web documents: an open-source platform and testbed

The LivingKnowledge project aimed to enhance the current state of the art in search, retrieval and knowledge management on the web by advancing the use of sentiment and opinion analysis within multimedia applications. To achieve this aim, a diverse set of novel and complementary analysis techniques have been integrated into a single, but extensible software platform on which such applications can be built. The platform combines state-of-the-art techniques for extracting facts, opinions and sentiment from multimedia documents, and unlike earlier platforms, it exploits both visual and textual techniques to support multimedia information retrieval. Foreseeing the usefulness of this software in the wider community, the platform has been made generally available as an open-source project. This paper describes the platform design, gives an overview of the analysis algorithms integrated into the system and describes two applications that utilise the system for multimedia information retrieval

Gazo bunseki to kanren joho o riyoshita gazo imi rikai ni kansuru kenkyu

制度:新 ; 報告番号:甲3514号 ; 学位の種類:博士(国際情報通信学) ; 授与年月日:2012/2/8 ; 早大学位記番号:新585