Automatic Image Annotation Problem Research Articles

Image retrieval based on weighted nearest neighbor tag prediction

Abstract With the development of communication and computer technology, the application of big data technology has become increasingly widespread. Reasonable, effective, and fast retrieval methods for querying information from massive data have become an important content of current research. This article provides an image retrieval method based on the weighted nearest neighbor label prediction for the problem of automatic image annotation and keyword image retrieval. In order to improve the performance of the test method, scientific experimental verification was implemented. The nearest neighbor weights are determined by maximizing the training image annotation, and experiments are carried out from multiple angles based on the Mahalanobis metric learning integration model. The experimental results show that the proposed tag correlation prediction propagation model has obvious improvements in accuracy, recall rate, break-even point, and overall average accuracy performance compared with other widely used algorithm models.

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

Abstract Nearest Neighbor method (KNN) is a typical method to solve the problem of automatic image annotation (AIA). However, traditional AIA methods based on KNN only consider the relationships among images and labels. In this paper, we propose an improved KNN image annotation method based on a tag semantic extension model (TSEM). Our approach uses the convolutional neural network (CNN) to extract image features and predicts image tags automatically via nearest features. Different from existing work, the proposed method considers correlations among images, correlations between images and labels and those among labels. Additionally, a label quantity prediction (LQP) model is proposed to predict the number of tags, which further improves the tag prediction accuracy. Comparison experiments were performed on three typical image datasets Corel5k, ESP game and laprtc12. Experimental results show that the average F1 of our model is 0.427, which outperforms the state-of-the-art KNN image annotation methods.

Kernel-Based Multiview Joint Sparse Coding for Image Annotation

It remains a challenging task for automatic image annotation problem due to the semantic gap between visual features and semantic concepts. To reduce the gap, this paper puts forward a kernel-based multiview joint sparse coding (KMVJSC) framework for image annotation. In KMVJSC, different visual features as well as label information are considered as distinct views and are mapped to an implicit kernel space, in which the original nonlinear separable data become linearly separable. Then, all the views are integrated into a multiview joint sparse coding framework aiming to find a set of optimal sparse representations and discriminative dictionaries adaptively, which can effectively employ the complementary information of different views. An optimization algorithm is presented by extending K-singular value decomposition (KSVD) and accelerated proximal gradient (APG) algorithms to the kernel multiview framework. In addition, a label propagation scheme using the sparse reconstruction and weighted greedy label transfer algorithm is also proposed. Comparative experiments on three datasets have demonstrated the competitiveness of proposed approach compared with other related methods.

АВТОМАТИЧЕСКОЕ АННОТИРОВАНИЕ ИЗОБРАЖЕНИЙ НА ОСНОВЕ ОДНОРОДНЫХ ТЕКСТОВО-ВИЗУАЛЬНЫХ ГРУПП

Purpose: The problem of automatic image annotation is not trivial. The training images often contain unbalanced and incomplete annotations, leading to a semantic gap between the visual features and textual description of an image. The existing methods include computationally complex algorithms which optimize the visual features and annotate a new image using all the training images and keywords, potentially reducing the accuracy. A compact visual descriptor should be developed, along with a method for choosing a group of the most informative training images for each test image. Results: A methodology for automatic image annotation is formulated, based on searching for a posteriori probability keyword association with a visual image descriptor. Six global descriptors combined in a single descriptor were obtained. The size of this single descriptor was reduced down to several hundred elements using principal component analysis. The experimental results showed an improvement of the annotation precision by 7% and a recall by 1%. Practical relevance: The compact handle visual method and automatic annotation of images based on the formation of homogeneous textual-visual groups can be used in Internet retrieval systems to improve the image search quality.

Automated Image Annotation for Semantic Indexing and Retrieval of Medical Images

Medical image retrieval to search for clinically relevant and visually similar images depicting suspecious lesions have been attracting research interest. Content-based image retrieval (CBIR) is an important alternate and complement to traditional text-based retrieval using keywords. We have implemented CBIR system based on effective use of texture information within the images obtained by statistical cooccurrence matrix method. Also, the method is improved by bridging the semantic gap between low-level visual features and the high-level semantic concepts using automated image annotations. In this paper, we have proposed a classification-based multi-class multi-label semantic model and the corresponding learning procedure to address the problem of automatic image annotation using J48 decision tree classifier and show its application to medical image retrieval. Hash structure is used to index images. Eucledian distance measure is used for similarity measurement. Both the methods are compared using precision and recall measures. Semantic indexing is shown to outperform CBIR for MR-T2 axial brain images.

Multi-class particle swarm model selection for automatic image annotation

This article describes the application of particle swarm model selection (PSMS) to the problem of automatic image annotation (AIA). PSMS can be considered a black-box tool for the selection of effective classifiers in binary classification problems. We face the AIA problem as one of multi-class classification, considering a one-vs-all (OVA) strategy. OVA makes a multi-class problem into a series of binary classification problems, each of which deals with whether a region belongs to a particular class or not. We use PSMS to select the models that compose the OVA classifier and propose a new technique for making multi-class decisions from the selected classifiers. This way, effective classifiers can be obtained in acceptable times; specific methods for preprocessing, feature selection and classification are selected for each class; and, most importantly, very good annotation performance can be obtained. We present experimental results in six data sets that give evidence of the validity of our approach; to the best of our knowledge the results reported herein are the best obtained so far in the data sets we consider. It is important to emphasize that despite the application domain we consider is AIA, nothing restricts us of applying the methods described in this article to any other multi-class classification problem. .

Content-based histopathology image retrieval using a kernel-based semantic annotation framework

Large amounts of histology images are captured and archived in pathology departments due to the ever expanding use of digital microscopy. The ability to manage and access these collections of digital images is regarded as a key component of next generation medical imaging systems. This paper addresses the problem of retrieving histopathology images from a large collection using an example image as query. The proposed approach automatically annotates the images in the collection, as well as the query images, with high-level semantic concepts. This semantic representation delivers an improved retrieval performance providing more meaningful results. We model the problem of automatic image annotation using kernel methods, resulting in a unified framework that includes: (1) multiple features for image representation, (2) a feature integration and selection mechanism (3) and an automatic semantic image annotation strategy. An extensive experimental evaluation demonstrated the effectiveness of the proposed framework to build meaningful image representations for learning and useful semantic annotations for image retrieval.

Improved Resulted Word Counts Optimizer for Automatic Image Annotation Problem

Automatic Image Annotation is an important research topic in pattern recognition area. There are many different approaches to Automatic Image Annotation. In many of these approaches a key problem is determining correct word distributions in the generated annotations. Incorrect word distributions (word frequencies) results in large reduction of annotation quality. The paper presents a generic approach to find correct word frequencies. The approach may be used with many automatic image annotators, based on various machine learning paradigms. Proposed method is an improved version of our already presented method, called Greedy Resulted Word Counts Optimization. The key differences and novelties in the paper are: the concept of border support values and a method of optimal step selection in the optimization routine. Optimal step selection allows to reduce the number of computations during the optimization procedure, comparing to old Greedy Resulted Word Counts Optimization method.

A probabilistic semantic model for image annotation and multi-modal image retrieval

This paper addresses automatic image annotation problem and its application to multi-modal image retrieval. The contribution of our work is three-fold. (1) We propose a probabilistic semantic model in which the visual features and the textual words are connected via a hidden layer which constitutes the semantic concepts to be discovered to explicitly exploit the synergy among the modalities. (2) The association of visual features and textual words is determined in a Bayesian framework such that the confidence of the association can be provided. (3) Extensive evaluation on a large-scale, visually and semantically diverse image collection crawled from Web is reported to evaluate the prototype system based on the model. In the proposed probabilistic model, a hidden concept layer which connects the visual feature and the word layer is discovered by fitting a generative model to the training image and annotation words through an Expectation-Maximization (EM) based iterative learning procedure. The evaluation of the prototype system on 17,000 images and 7736 automatically extracted annotation words from crawled Web pages for multi-modal image retrieval has indicated that the proposed semantic model and the developed Bayesian framework are superior to a state-of-the-art peer system in the literature.