Automatic image annotation for semantic image retrieval (original) (raw)
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Review: Automatic Image Annotation for Semantic Image Retrieval
Lecture Notes in Computer Science, 2018
Nowadays, the number of digital data sets grows exponentially. Hence, the need to conceive efficient and powerful image indexation and retrieval systems grows as well. Automatic image annotation was adopted by several research as the emerging trend in image retrieval area. Actually, it is considered as the best solution that combines the content-based techniques by using low-level image features and text-based techniques exploiting textual annotations, associated to the image. In this way, the semantic gap between low-level image features and high-level semantics will be reduced. This paper presents a review of image retrieval approaches, by focusing especially on the automatic image annotation methods, in order to analyse the impact of annotations and associating semantics to the visual data for an image retrieval process.
Semantic Scene Classification for Image Annotation
Nowadays The problem of annotating an image falls under pattern classification and computer vision methods. Various systems have been proposed for the purpose of image annotation, content based retrieval, and image classification and indexing. Most of these systems are based on low level features such as color and texture statistics. We believe that the detection of semantically meaningfully regions in digital photographs could be productively employed in classification and retrieval systems for image databases. The image retrieval problem has not been successfully solved in spite of decades of work, dozens of research prototypes and commercial products that have been developed lately. In addition to its primary goals, consistent, cost-effective, fast, intelligent annotation of visual data, the method proposed can also be used to improve the performance of subsequent image query and retrieval operations on the annotated image repository. Pure content based image retrieval (CBIR) also falls short of providing an adequate solution to the problem, mostly due to the limitations of current computer vision techniques in bridging the gap between visual features and their semantic meaning. There is a clear need to cleverly combine the two in order to yield a better solution.
Automatic Annotation of Digital Images using Colour Structure and Edge Direction
2007 IEEE International Conference on Signal Processing and Communications, 2007
The focus of this paper is on automatic annotation for semantic image retrieval. This work is aimed at identifying visual descriptors that are most relevant, effective and suitable for semantic annotation tasks. We propose an image annotation system based on support vector machines and a combination of descriptors that includes a gradient direction histogram and several MPEG-7 visual descriptors. The system is tested on a large database of 7200 cityscape and landscape images. The results indicate that when descriptors are used individually, the proposed gradient direction histogram performs best. However, when descriptors are combined, the accuracy is improved. The presented results confirm that combining the gradient direction histogram and colour structure produces the best results.
Review: Automatic Semantic Image Annotation
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2016
There are many approaches for automatic annotation in digital images. Nowadays digital photography is a common technology for capturing and archiving images because of the digital cameras and storage devices reasonable price. As amount of the digital images increase, the problem of annotating a specific image becomes a critical issue. Automated image annotation is creating a model capable of assigning terms to an image in order to describe its content. There are many image annotation techniques that seek to find the correlation between words and image features such as color, shape, and texture to provide an automatically correct annotation words to images which provides an alternative to the time consuming work of manual image annotation. This paper aims to cover a review on different Models (MT, CRM, CSD-Prop, SVD-COS and CSD-SVD) for automating the process of image annotation as an intermediate step in image retrieval process using Corel 5k images.
Semantic Classification of Web Images for Efficient Image Retrieval
International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06), 2005
Grouping images into semantically meaningful categories using basic low-level visual features is a challenging and important problem in content-based image retrieval. The enormity and diversity of the visual contents on the web images adds another dimension to this challenging task. Moreover, the retrieval of web images cannot be easily achieved with images of other than trivial collections, and therefore one needs to put more cognitive load on the users. Based on the groupings, effective indices can however be built for an image database. In this paper, we show how a specific high-level classification problem can be solved from relatively basic low-level visual features geared for the particular classes. We have developed a procedure to qualitatively measure the saliency of a feature towards a classification problem based on the discrimination power of the HSV color histograms, which capture the visual characteristics of each of the images were computed. We found that the HSV color histogram, mainly the hue component, has the most discriminative power for the classification problem of our interest. A kmeans classifier is used for the classification, which results in an accuracy of 90.5% when evaluated on an image database of 2,738 web images. The images are classified as full faces, natural sceneries, events and city images. Our final goal is to use this classification knowledge to enhance the performance of content-based image retrievals by filtering out images from irrelevant classes during the matching.
IJERT-Semantic Categorization and Retrieval of Natural Scene Images
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/semantic-categorization-and-retrieval-of-natural-scene-images https://www.ijert.org/research/semantic-categorization-and-retrieval-of-natural-scene-images-IJERTV2IS100944.pdf In this paper, we present an approach for the retrieval of natural scenes based on a semantic modelling. In this Semantic modelling stands for the classification of local image regions into semantic classes as grass, rocks or foliage and the subsequent summary in the local image regions are classified using low level features into semantic concept classes such as water, sky or sand. This paper proposes a method for semantic categorization and retrieval of natural scene images or picture with and without people. Image retrieval systems usually represent images by a collection of low-level features such as colour, texture, edge positions and spatial relationships in the image. These features are used to compute the similarity between a picture or image selected by the user and the picture or images in the database. We have attempts to provide a comprehensive survey of the latest mechanical achievements in high-level semantic-based image retrieval.
Semantic image retrieval and auto-annotation by converting keyword space to image space
Multi-Media Modelling Conference …, 2006
In this paper, we propose a novel strategy at an abstract level by combining textual and visual clustering results to retrieve images using semantic keywords and auto-annotate images based on similarity with existing keywords. Our main hypothesis is that images that fall in to the same text-cluster can be described with common visual features of those images. In order to implement this hypothesis, we set out to estimate the common visual features in the textually clustered images. When given an un-annotated image, we find the best image match in the different textual clusters by processing their low-level features. Experiments have demonstrated that good accuracy of proposal and its high potential use in annotation of images and for improvement of content based image retrieval.
Pattern Recognition and Image Analysis, 2016
A large percentage of photos on the Internet cannot be reached by search engines because of the semantic gap due to the absence of textual meta data. Despite of decades of research, neither model based approaches can provide quality annotation to images. Many segmentation algorithms use a low level predi cates to control the homogeneity of the regions. So, the resulting regions are not always being semantically compact. The first proposed approach to resolve this problem is to regroup the adjacent region of image. Many features extraction method and classifiers are also used singly, with modest results, for automatic image annotation. The second proposed approach is to select and combine together some efficient descriptors and classifiers. This document provides a hybrid semantic annotation system that combines both approaches in hopes of increasing the accuracy of the resulting annotations. The color histograms, Texture, GIST and invariant moments, used as features extraction methods, are combined together with multi class support vec tor machine, Bayesian networks, Neural networks and nearest neighbor classifiers, in order to annotate the image content with the appropriate keywords. The accuracy of the proposed approach is supported by the good experimental results obtained from two image databases (ETH 80 and coil 100 databases).
Semantic Approach to Image Database Classification and Retrieval
2003
This paper demonstrates an approach to image retrieval founded on classifying image regions hierarchically based on their semantics (e.g., sky, snow, rocks, etc.) that resemble peoples' perception rather than on low-level features (e.g., color, texture, shape, etc.). Particularly, we consider to automatically classify regions of outdoor images based on their semantics using the support vector machines (SVMs) tool. First, image regions are segmented using the hill-climbing method. Then, those regions are classified by the SVMs. The SVMs learn the semantics of specified classes from a test database of image regions. Such semantic classification allows the implementation of intuitive query interface. As we show in our experiments, the high precision of semantic classification justifies the feasibility of our approach.
Image Retrieval Using Visual Image Features and Automatic Image Annotation
2016
In recent few years, the exponential growth in the number of multimedia databases makes Content-Based Image Retrieval (CBIR) a challenging research area. In image classification and retrieval problems, the extraction of a meaningful image descriptor is an active research area. In CBIR, feature vectors are used to represent the images that are commonly based on color, texture, shape and spatial layout of the image. The mid-level local feature detectors are applied to map the image representation in a high-dimension feature space. The Bag of Features (BoF) based image representation model is