3,925 research outputs found

    Image annotation and retrieval based on multi-modal feature clustering and similarity propagation.

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    The performance of content-based image retrieval systems has proved to be inherently constrained by the used low level features, and cannot give satisfactory results when the user\u27s high level concepts cannot be expressed by low level features. In an attempt to bridge this semantic gap, recent approaches started integrating both low level-visual features and high-level textual keywords. Unfortunately, manual image annotation is a tedious process and may not be possible for large image databases. In this thesis we propose a system for image retrieval that has three mains components. The first component of our system consists of a novel possibilistic clustering and feature weighting algorithm based on robust modeling of the Generalized Dirichlet (GD) finite mixture. Robust estimation of the mixture model parameters is achieved by incorporating two complementary types of membership degrees. The first one is a posterior probability that indicates the degree to which a point fits the estimated distribution. The second membership represents the degree of typicality and is used to indentify and discard noise points. Robustness to noisy and irrelevant features is achieved by transforming the data to make the features independent and follow Beta distribution, and learning optimal relevance weight for each feature subset within each cluster. We extend our algorithm to find the optimal number of clusters in an unsupervised and efficient way by exploiting some properties of the possibilistic membership function. We also outline a semi-supervised version of the proposed algorithm. In the second component of our system consists of a novel approach to unsupervised image annotation. Our approach is based on: (i) the proposed semi-supervised possibilistic clustering; (ii) a greedy selection and joining algorithm (GSJ); (iii) Bayes rule; and (iv) a probabilistic model that is based on possibilistic memebership degrees to annotate an image. The third component of the proposed system consists of an image retrieval framework based on multi-modal similarity propagation. The proposed framework is designed to deal with two data modalities: low-level visual features and high-level textual keywords generated by our proposed image annotation algorithm. The multi-modal similarity propagation system exploits the mutual reinforcement of relational data and results in a nonlinear combination of the different modalities. Specifically, it is used to learn the semantic similarities between images by leveraging the relationships between features from the different modalities. The proposed image annotation and retrieval approaches are implemented and tested with a standard benchmark dataset. We show the effectiveness of our clustering algorithm to handle high dimensional and noisy data. We compare our proposed image annotation approach to three state-of-the-art methods and demonstrate the effectiveness of the proposed image retrieval system

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

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    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

    Learning Multimodal Latent Attributes

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    Abstract—The rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multi-modal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we (1) introduce a concept of semi-latent attribute space, expressing user-defined and latent attributes in a unified framework, and (2) propose a novel scalable probabilistic topic model for learning multi-modal semi-latent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multi-task learning, learning with label noise, N-shot transfer learning and importantly zero-shot learning

    Deep Fragment Embeddings for Bidirectional Image Sentence Mapping

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    We introduce a model for bidirectional retrieval of images and sentences through a multi-modal embedding of visual and natural language data. Unlike previous models that directly map images or sentences into a common embedding space, our model works on a finer level and embeds fragments of images (objects) and fragments of sentences (typed dependency tree relations) into a common space. In addition to a ranking objective seen in previous work, this allows us to add a new fragment alignment objective that learns to directly associate these fragments across modalities. Extensive experimental evaluation shows that reasoning on both the global level of images and sentences and the finer level of their respective fragments significantly improves performance on image-sentence retrieval tasks. Additionally, our model provides interpretable predictions since the inferred inter-modal fragment alignment is explicit
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