Semantics and statistics for automated image annotation

Automated image annotation consists of a number of techniques that aim to find the correlation between words and image features such as colour, shape, and texture to provide correct annotation words to images. In particular, approaches based on Bayesian theory use machine-learning techniques to learn statistical models from a training set of pre-annotated images and apply them to generate annotations for unseen images. The focus of this thesis lies in demonstrating that an approach, which goes beyond learning the statistical correlation between words and visual features and also exploits information about the actual semantics of the words used in the annotation process, is able to improve the performance of probabilistic annotation systems. Specifically, I present three experiments. Firstly, I introduce a novel approach that automatically refines the annotation words generated by a non-parametric density estimation model using semantic relatedness measures. Initially, I consider semantic measures based on co-occurrence of words in the training set. However, this approach can exhibit limitations, as its performance depends on the quality and coverage provided by the training data. For this reason, I devise an alternative solution that combines semantic measures based on knowledge sources, such as WordNet and Wikipedia, with word co-occurrence in the training set and on the web, to achieve statistically significant results over the baseline. Secondly, I investigate the effect of using semantic measures inside an evaluation measure that computes the performance of an automated image annotation system, whose annotation words adopt the hierarchical structure of an ontology. This is the case of the ImageCLEF2009 collection. Finally, I propose a Markov Random Field that exploits the semantic context dependencies of the image. The best result obtains a mean average precision of 0.32, which is consistent with the state-of-the-art in automated image annotation for the Corel 5k dataset. </br

Data linking for the Semantic Web

By specifying that published datasets must link to other existing datasets, the 4th linked data principle ensures a Web of data and not just a set of unconnected data islands. The authors propose in this paper the term data linking to name the problem of finding equivalent resources on the Web of linked data. In order to perform data linking, many techniques were developed, finding their roots in statistics, database, natural language processing and graph theory. The authors begin this paper by providing background information and terminological clarifications related to data linking. Then a comprehensive survey over the various techniques available for data linking is provided. These techniques are classified along the three criteria of granularity, type of evidence, and source of the evidence. Finally, the authors survey eleven recent tools performing data linking and we classify them according to the surveyed techniques

Automated Social Text Annotation With Joint Multilabel Attention Networks

Automated social text annotation is the task of suggesting a set of tags for shared documents on social media platforms. The automated annotation process can reduce users' cognitive overhead in tagging and improve tag management for better search, browsing, and recommendation of documents. It can be formulated as a multilabel classification problem. We propose a novel deep learning-based method for this problem and design an attention-based neural network with semantic-based regularization, which can mimic users' reading and annotation behavior to formulate better document representation, leveraging the semantic relations among labels. The network separately models the title and the content of each document and injects an explicit, title-guided attention mechanism into each sentence. To exploit the correlation among labels, we propose two semantic-based loss regularizers, i.e., similarity and subsumption, which enforce the output of the network to conform to label semantics. The model with the semantic-based loss regularizers is referred to as the joint multilabel attention network (JMAN). We conducted a comprehensive evaluation study and compared JMAN to the state-of-the-art baseline models, using four large, real-world social media data sets. In terms of F 1 , JMAN significantly outperformed bidirectional gated recurrent unit (Bi-GRU) relatively by around 12.8%-78.6% and the hierarchical attention network (HAN) by around 3.9%-23.8%. The JMAN model demonstrates advantages in convergence and training speed. Further improvement of performance was observed against latent Dirichlet allocation (LDA) and support vector machine (SVM). When applying the semantic-based loss regularizers, the performance of HAN and Bi-GRU in terms of F 1 was also boosted. It is also found that dynamic update of the label semantic matrices (JMAN d ) has the potential to further improve the performance of JMAN but at the cost of substantial memory and warrants further study

Multi modal multi-semantic image retrieval

PhDThe rapid growth in the volume of visual information, e.g. image, and video can overwhelm users’ ability to find and access the specific visual information of interest to them. In recent years, ontology knowledge-based (KB) image information retrieval techniques have been adopted into in order to attempt to extract knowledge from these images, enhancing the retrieval performance. A KB framework is presented to promote semi-automatic annotation and semantic image retrieval using multimodal cues (visual features and text captions). In addition, a hierarchical structure for the KB allows metadata to be shared that supports multi-semantics (polysemy) for concepts. The framework builds up an effective knowledge base pertaining to a domain specific image collection, e.g. sports, and is able to disambiguate and assign high level semantics to ‘unannotated’ images. Local feature analysis of visual content, namely using Scale Invariant Feature Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’ model (BVW) as an effective method to represent visual content information and to enhance its classification and retrieval. Local features are more useful than global features, e.g. colour, shape or texture, as they are invariant to image scale, orientation and camera angle. An innovative approach is proposed for the representation, annotation and retrieval of visual content using a hybrid technique based upon the use of an unstructured visual word and upon a (structured) hierarchical ontology KB model. The structural model facilitates the disambiguation of unstructured visual words and a more effective classification of visual content, compared to a vector space model, through exploiting local conceptual structures and their relationships. The key contributions of this framework in using local features for image representation include: first, a method to generate visual words using the semantic local adaptive clustering (SLAC) algorithm which takes term weight and spatial locations of keypoints into account. Consequently, the semantic information is preserved. Second a technique is used to detect the domain specific ‘non-informative visual words’ which are ineffective at representing the content of visual data and degrade its categorisation ability. Third, a method to combine an ontology model with xi a visual word model to resolve synonym (visual heterogeneity) and polysemy problems, is proposed. The experimental results show that this approach can discover semantically meaningful visual content descriptions and recognise specific events, e.g., sports events, depicted in images efficiently. Since discovering the semantics of an image is an extremely challenging problem, one promising approach to enhance visual content interpretation is to use any associated textual information that accompanies an image, as a cue to predict the meaning of an image, by transforming this textual information into a structured annotation for an image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct types of information representation and modality, there are some strong, invariant, implicit, connections between images and any accompanying text information. Semantic analysis of image captions can be used by image retrieval systems to retrieve selected images more precisely. To do this, a Natural Language Processing (NLP) is exploited firstly in order to extract concepts from image captions. Next, an ontology-based knowledge model is deployed in order to resolve natural language ambiguities. To deal with the accompanying text information, two methods to extract knowledge from textual information have been proposed. First, metadata can be extracted automatically from text captions and restructured with respect to a semantic model. Second, the use of LSI in relation to a domain-specific ontology-based knowledge model enables the combined framework to tolerate ambiguities and variations (incompleteness) of metadata. The use of the ontology-based knowledge model allows the system to find indirectly relevant concepts in image captions and thus leverage these to represent the semantics of images at a higher level. Experimental results show that the proposed framework significantly enhances image retrieval and leads to narrowing of the semantic gap between lower level machinederived and higher level human-understandable conceptualisation

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

Simulation-to-Real domain adaptation with teacher-student learning for endoscopic instrument segmentation

Purpose: Segmentation of surgical instruments in endoscopic videos is essential for automated surgical scene understanding and process modeling. However, relying on fully supervised deep learning for this task is challenging because manual annotation occupies valuable time of the clinical experts. Methods: We introduce a teacher-student learning approach that learns jointly from annotated simulation data and unlabeled real data to tackle the erroneous learning problem of the current consistency-based unsupervised domain adaptation framework. Results: Empirical results on three datasets highlight the effectiveness of the proposed framework over current approaches for the endoscopic instrument segmentation task. Additionally, we provide analysis of major factors affecting the performance on all datasets to highlight the strengths and failure modes of our approach. Conclusion: We show that our proposed approach can successfully exploit the unlabeled real endoscopic video frames and improve generalization performance over pure simulation-based training and the previous state-of-the-art. This takes us one step closer to effective segmentation of surgical tools in the annotation scarce setting.Comment: Accepted at IPCAI202