Jointing visual-semantic embeddings (VSE) have become a research hotpot for the task of image annotation, which suffers from the issue of semantic gap, i.e., the gap between images' visual features (low-level) and labels' semantic features (high-level). This issue will be even more challenging if visual features cannot be retrieved from images, that is, when images are only denoted by numerical IDs as given in some real datasets. The typical way of existing VSE methods is to perform a uniform sampling method for negative examples that violate the ranking order against positive examples, which requires a time-consuming search in the whole label space. In this paper, we propose a fast adaptive negative sampler that can work well in the settings of no figure pixels available. Our sampling strategy is to choose the negative examples that are most likely to meet the requirements of violation according to the latent factors of images. In this way, our approach can linearly scale up to large datasets. The experiments demonstrate that our approach converges 5.02x faster than the state-of-the-art approaches on OpenImages, 2.5x on IAPR-TCI2 and 2.06x on NUS-WIDE datasets, as well as better ranking accuracy across datasets.
Semi-automatic annotations can produce automatic annotation to some extent, but also require to build fundamental structures with the involvement of human beings. For example, @cite_11 propose a layered structure to build image ontology for annotations, where low-level features of images are selected by the bottom layer. By abstracting low-level features up to high-level features, it connects the semantic feature of images with appropriate annotations. However, the building of image ontology requires expert knowledge, and may be domain-specific. @cite_14 formulate image annotation as a multi-label learning problem, and develop a semi-automatic annotating system. For a given image, their system initially chooses some keywords from a vocabulary as labels, and then refines these labels in the light of user feedback.
@cite_14 @cite_11
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Automatic image annotation systems available in the literature concatenate color, texture and or shape features in a single feature vector to learn a set of high level semantic categories using a single learning machine. This approach is quite naive to map the visual features to high level semantic information concerning the categories. Concatenation of many features with different visual properties and wide dynamical ranges may result in curse of dimensionality and redundancy problems. Additionally, it usually requires normalization which may cause an undesirable distortion in the feature space. An elegant way of reducing the effects of these problems is to design a dedicated feature space for each image category, depending on its content, and learn a range of visual properties of the whole image from a variety of feature sets. For this purpose, a two-layer ensemble learning system, called Supervised Annotation by Descriptor Ensemble (SADE), is proposed. SADE, initially, extracts a variety of low-level visual descriptors from the image. Each descriptor is, then, fed to a separate learning machine in the first layer. Finally, the meta-layer classifier is trained on the output of the first layer classifiers and the images are annotated by using the decision of the meta-layer classifier. This approach not only avoids normalization, but also reduces the effects of dimensional curse and redundancy. The proposed system outperforms a state-of-the-art automatic image annotation system, in an equivalent experimental setup. Automatic image annotation aims at predicting a set of textual labels for an image that describe its semantics. These are usually taken from an annotation vocabulary of few hundred labels. Because of the large vocabulary, there is a high variance in the number of images corresponding to different labels ("class-imbalance"). Additionally, due to the limitations of manual annotation, a significant number of available images are not annotated with all the relevant labels ("weak-labelling"). These two issues badly affect the performance of most of the existing image annotation models. In this work, we propose 2PKNN, a two-step variant of the classical K-nearest neighbour algorithm, that addresses these two issues in the image annotation task. The first step of 2PKNN uses "image-to-label" similarities, while the second step uses "image-to-image" similarities; thus combining the benefits of both. Since the performance of nearest-neighbour based methods greatly depends on how features are compared, we also propose a metric learning framework over 2PKNN that learns weights for multiple features as well as distances together. This is done in a large margin set-up by generalizing a well-known (single-label) classification metric learning algorithm for multi-label prediction. For scalability, we implement it by alternating between stochastic sub-gradient descent and projection steps. Extensive experiments demonstrate that, though conceptually simple, 2PKNN alone performs comparable to the current state-of-the-art on three challenging image annotation datasets, and shows significant improvements after metric learning. Automatic image annotation is the task of automatically assigning words to an image that describe the content of the image. Machine learning approaches have been explored to model the association between words and images from an annotated set of images and generate annotations for a test image. The paper proposes methods to use a hierarchy defined on the annotation words derived from a text ontology to improve automatic image annotation and retrieval. Specifically, the hierarchy is used in the context of generating a visual vocabulary for representing images and as a framework for the proposed hierarchical classification approach for automatic image annotation. The effect of using the hierarchy in generating the visual vocabulary is demonstrated by improvements in the annotation performance of translation models. In addition to performance improvements, hierarchical classification approaches yield well to constructing multimedia ontologies. Image annotations allow users to access a large image database with textual queries. There have been several studies on automatic image annotation utilizing machine learning techniques, which automatically learn statistical models from annotated images and apply them to generate annotations for unseen images. One common problem shared by most previous learning approaches for automatic image annotation is that each annotated word is predicated for an image independently from other annotated words. In this paper, we proposed a coherent language model for automatic image annotation that takes into account the word-to-word correlation by estimating a coherent language model for an image. This new approach has two important advantages: 1) it is able to automatically determine the annotation length to improve the accuracy of retrieval results, and 2) it can be used with active learning to significantly reduce the required number of annotated image examples. Empirical studies with Corel dataset are presented to show the effectiveness of the coherent language model for automatic image annotation.