Image Annotation in a Progressive Way

In order to investigate the performance of visual feature extraction method for automatic image annotation, three visual feature extraction methods, namely discrete cosine transform, Gabor transform and discrete wavelet transform, are studied in this paper. These three methods are used to extract low-level visual feature vectors from images in a given database separately, then these feature vectors are mapped to high-level semantic words to annotate images with labels in a given semantic label set. As it is more efficient to depict the visual features of an image by the feature distribution than to resort to image segmentation technology for semantic image blocks, this paper is going to find out which of the three feature extraction methods performs better in image annotation based on the distribution of feature vectors from the image. The performance of three different kinds of feature extraction method is fully analyzed, and it is found that discrete cosine transform method is more suitable for Gaussian mixture model in automatic image annotation.

Automatic Image Annotation is important research topic in machine vision as it enables one to retrieve images from large databases by using textual queries. In recent years many machine learning techniques have been proposed to build detectors of concepts present on the images. In this paper we present a novel approach for image auto-annotation based on transfer of annotations from most similar images to the query image. We model image features by Multivariate Gaussian Distribution and measure distance between images by using Jensen-Shannon divergence. In spite of its simplicity, the proposed solution outperforms the state-of-the-art methods for image annotation and thus can be used as a baseline for developing other more elaborate methods.KeywordsQuery ImageSimilar ImageImage AnnotationSemantic LabelAutomatic Image AnnotationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A Survey on Automatic Image Annotation and Trends of the New Age

Automatic image annotation has been extensively studied, mostly from a content-based approach, whose effectiveness is restricted by the “semantic gap” between low-level image features and semantic annotations, and by the irrelevance of annotations to image content. We propose a social diffusion analysis approach to image annotation, which exploits abundant social diffusion records about how images are disseminated within online social networks. Specifically, we propose a common-interest model to analyze social diffusion records, with the assumption that the diffusion pattern of an image in social networks is highly related to the relevance between image annotations and user preferences. In our proposed model, user preferences are represented as common interests of pairwise users instead of individual user interests. We find the notion of common interests not only facilitates the analysis of social diffusion patterns, but also leads to more accurate profiling of user preferences compared to individual interests. Based on the common-interest model, we design an image annotation framework via social diffusion analysis, which consists of the mining of common interests from social diffusion records, the feature extraction from diffusion graphs and common interests, and the automatic annotation by the learning-to-rank method. Experimental results on real-world data sets show that our proposed common-interest based approach outperforms individual-interest based methods, and also achieves superior performance than state-of-the-art content-based image annotation methods.

A survey and analysis on automatic image annotation

Automatic image annotation is a critical and challenging problem in pattern recognition and image understanding areas. There are some problems in existing automatic image annotation areas. For example, the size of unlabeled data is much larger than the labeled data. Besides, most image annotation models can only use one kind of image segmentation strategy and certain image description method. According to the above problems, an automatic image annotation model based on Co-training is proposed. In this model, four independent feature properties are constructed and then four corresponding sub-classifiers are built. In this way, different image segmentation strategies and feature representation methods can be integrated into a unified framework. An adaptive algorithm based on vote and consistency is proposed to extend the training dataset. The proposed method use Co-training algorithm and mass unlabeled data to improve the performance of automatic image annotation. Experiments conducted on Corel 5 K dataset verify the effectiveness of proposed method.

This article presents an automatic image level annotation approach that takes advantage of both context and semantics presented in segmented images. The proposed approach is based on the optimization of classes’ scores using particle swarm optimization. In addition, random forest classifier and normalized cuts algorithm have been applied for automatic image classification, annotation, and clustering. For the proposed approach, each input image is segmented using the normalized cuts segmentation algorithm in order to create a descriptor for each segment. Two parameter selection models have been selected for particle swarm optimization algorithm and many voting techniques have been implemented to find the most suitable set of annotation words per image. Experimental results, using Corel5k benchmark annotated images dataset, demonstrate that applying optimization algorithms along with random forest classifier achieved noticeable increase in image annotation performance measures compared to related researches on the same dataset.

Joint multi-view representation and image annotation via optimal predictive subspace learning

The explosive growth of digital images requires effective methods to manage these images. Among various existing methods, automatic image annotation has proved to be an important technique for image management tasks, e.g., image retrieval over large-scale image databases. Automatic image annotation has been widely studied during recent years and a considerable number of approaches have been proposed. However, the performance of these methods is yet to be satisfactory, thus demanding more effort on research of image annotation. In this paper, we propose a novel semi supervised framework built upon feature selection for automatic image annotation. Our method aims to jointly select the most relevant features from all the data points by using a sparsity-based model and exploiting both labeled and unlabeled data to learn the manifold structure. Our framework is able to simultaneously learn a robust classifier for image annotation by selecting the discriminating features related to the semantic concepts. To solve the objective function of our framework, we propose an efficient iterative algorithm. Extensive experiments are performed on different real-world image datasets with the results demonstrating the promising performance of our framework for automatic image annotation.

Even within constrained domains like medicine there are no truly generic methods for automatic image parsing and annotation. Despite the fact that the precision and sophistication of image understanding methods have improved to cope with the increasing amount and complexity of the data, the improvements have not resulted in more flexible or generic image understanding techniques. Instead, the analysis methods are object specific and modality dependent. Consequently, current image search techniques are still dependent on the manual and subjective association of keywords to images for retrieval. Manually annotating the vast numbers of images which are generated and archived in the medical practice is not an option.

Image distance metric learning based on neighborhood sets for automatic image annotation

Automatic image annotation based on an improved nearest neighbor technique with tag semantic extension model

Automatic image annotation is one of the research fields helping to extract the meaning of images, which aims at the production of a set of semantic annotations for an image to help better present the concept. Over the past few decades, researchers have developed a number of approaches for automatic image annotation. Nevertheless, previous studies have not fully accounted for visual features and annotated features. Therefore, it is still possible to achieve a better annotation performance by combining visual and annotated information. In this study, our aim is to associate multiple semantic tags with a given image. In particular, we detect how to obtain the image annotation by utilizing visual and annotated information. To take advantage of visual information, we first designed a modified neural network method to acquire the features of the image content. In addition, to obtain the annotated features, we exploit an aggregated network embedding approach that consists of annotation embedding, social embedding, profile embedding, and semantic embedding. Finally, to produce an accurate image annotation, we integrate the two aforementioned methods, that is, combining the visual and annotated information, to build a unified cooperative training framework. The experimental results on three real-world datasets clarify that our presented method is superior to the current popular image annotation approaches.

A Hierarchical Image Annotation Method Based on SVM and Semi-supervised EM

Since most computer vision approaches are now driven by machine learning, the current bottleneck is the annotation of images. This time-consuming task is usually performed manually after the acquisition of images. In this article, we assess the value of various egocentric vision approaches in regard to performing joint acquisition and automatic image annotation rather than the conventional two-step process of acquisition followed by manual annotation. This approach is illustrated with apple detection in challenging field conditions. We demonstrate the possibility of high performance in automatic apple segmentation (Dice 0.85), apple counting (88 percent of probability of good detection, and 0.09 true-negative rate), and apple localization (a shift error of fewer than 3 pixels) with eye-tracking systems. This is obtained by simply applying the areas of interest captured by the egocentric devices to standard, non-supervised image segmentation. We especially stress the importance in terms of time of using such eye-tracking devices on head-mounted systems to jointly perform image acquisition and automatic annotation. A gain of time of over 10-fold by comparison with classical image acquisition followed by manual image annotation is demonstrated.