Automatic Image Annotation Based on Particle Swarm Optimization and Support Vector Clustering

Hao, Zhangang; Ge, Hongwei; Gu, Tianpeng

doi:10.1155/2017/8493267

Cited by 7 publications

(5 citation statements)

References 35 publications

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“…Hao et al proposed an automatic image annotation method based on particle swarm optimization (PSO) and support vector clustering (SVC). They use PSO to optimize the SVC for automatic image annotation [ 9 ].…”

Section: Related Workmentioning

confidence: 99%

“…This paper specifically uses the support vector data description (SVDD) algorithm [ 32 ]. However, considering the literature [ 9 ] (our previous study), we used PSO to optimize the SVDD to improve the accuracy of the algorithm. Originally used to solve one-class classification problems, the SVDD has been later extended to solve multiple classification problems.…”

Section: Automatic Annotation Based On Visual Attention Mechanism mentioning

confidence: 99%

“…So, CBIR has not been used widely [ 2 – 5 ]. Image retrieval process can be indirectly changed into text retrieval by automatic image annotation [ 6 – 9 ]. That is, users need to provide only the query keywords, with the system returning the images associated with the keywords, which is in line with most users’ current habits.…”

Section: Introductionmentioning

confidence: 99%

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Visual attention mechanism and support vector machine based automatic image annotation

Hao

Wang

2018

PLoS ONE

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Automatic image annotation not only has the efficiency of text-based image retrieval but also achieves the accuracy of content-based image retrieval. Users of annotated images can locate images they want to search by providing keywords. Currently most automatic image annotation algorithms do not consider the relative importance of each region in the image, and some algorithms extract the image features as a whole. This makes it difficult for annotation words to reflect salient versus non-salient areas of the image. Users searching for images are usually only interested in the salient areas. We propose an algorithm that integrates a visual attention mechanism with image annotation. A preprocessing step divides the image into two parts, the salient regions and everything else, and the annotation step places a greater weight on the salient region. When the image is annotated, words relating to the salient region are given first. The support vector machine uses particle swarm optimization to annotate the images automatically. Experimental results show the effectiveness of the proposed algorithm.

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Section: Related Workmentioning

confidence: 99%

Section: Automatic Annotation Based On Visual Attention Mechanism mentioning

confidence: 99%

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Visual attention mechanism and support vector machine based automatic image annotation

Hao

Wang

2018

PLoS ONE

Self Cite

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“…This is a problem for traditional brocade material, which often contains valuable information behind its visualization, such as the fairy tales, stories, inheritors and textile skills associated with the objects. Additionally, in recent years, to improve the accuracy of generated descriptions of image sets, some research has focused on automatic annotation by using machine learning methods (Hao et al, 2017), which brings another explorative dimension to semantic description.…”

Section: Related Workmentioning

confidence: 99%

Interpreting and semantically describing Chinese traditional brocade: Xilankapu

Cheng

Yang

Jiang

et al. 2019

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Purpose The purpose of this paper is to introduce an interpreting schema and semantic description framework for a collection of images of Xilankapu, a traditional Chinese form of embroidered fabric and brocade artwork. Design/methodology/approach First, the authors interpret the artwork of Xilankapu through Gillian Rose’s “four site” theory by presenting how the brocades were made, how the patterns of Xilankapu are classified and the geometrical abstraction of visual images. To further describe the images of this type of brocade, this paper presents semantic descriptions that include objective–non-objective relations and a multi-layered semantic framework. Furthermore, the authors developed corresponding methods for scanning, storage and indexing images for retrieval. Findings As exploratory research on describing, preserving and indexing images of Xilankapu in the context of the preservation of cultural heritage, the authors collected 1,000+ images of traditional Xilankapu, classifying and storing some of the images in a database. They developed an index schema that combines concept- and content-based approaches according to the proposed semantic description framework. They found that the framework can describe, store and preserve semantic and non-semantic information of the same image. They relate the findings of this paper to future research directions for the digital preservation of traditional cultural heritages. Research limitations/implications The framework has been designed especially for brocade, and it needs to be extended to other types of cultural image. Originality/value The semantic description framework can describe connotative semantic information on Xilankapu. It can also assist the later information retrieval work in organizing implicit information about culturally related visual materials.

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“…Content-Based Image Retrieval approach automatically retrieves and index , 0 (2019) MATEC Web of Conferences https://doi.org/10.1051/matecconf/20192 255 1003 5501003 EAAI Conference 2018 different low-level features (colour, shape and texture) [5][6]. The need for large-scale image dataset annotation introduced the concept of Automatically Image Annotation (AIA) [7][8][9][10]. The AIA technique contains the good characteristics (advantages) from both traditional (text based and CBIR) annotated techniques through the keyword searching based on image content.…”

Section: Text-based Approach 2 Content-based Image Retrieval (Cbir) mentioning

confidence: 99%

Automatic Image Annotation for Small and Ad hoc Intelligent Applications using Raspberry Pi

et al. 2019

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The cutting-edge technology Machine Learning (ML) is successfully applied for Business Intelligence. Among the various pre-processing steps of ML, Automatic Image Annotation (also known as automatic image tagging or linguistic indexing) is the process in which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. Automatic Image Annotation (AIA) methods (which have appeared during the last several years) make a large use of many ML approaches. Clustering and classification methods are most frequently applied to annotate images. In addition, these proposed solutions require a high computational infrastructure. However, certain real-time applications (small and ad-hoc intelligent applications) for example, autonomous small robots, gadgets, drone etc. have limited computational processing capacity. These small and ad-hoc applications demand a more dynamic and portable way to automatically annotate data and then perform ML tasks (Classification, clustering etc.) in real time using limited computational power and hardware resources. Through a comprehensive literature study we found that most image pre-processing algorithms and ML tasks are computationally intensive, and it can be challenging to run them on an embedded platform with acceptable frame rates. However, Raspberry Pi is sufficient for AIA and ML tasks that are relevant to small and ad-hoc intelligent applications. In addition, few critical intelligent applications (which require high computational resources, for example, Deep Learning using huge dataset) are only feasible to run on more powerful hardware resources. In this study, we present the framework of “Automatic Image Annotation for Small and Ad-hoc Intelligent Application using Raspberry Pi” and propose the low-cost infrastructures (single node and multi node using Raspberry Pi) and software module (for Raspberry Pi) to perform AIA and ML tasks in real time for small and ad-hoc intelligent applications. The integration of both AIA and ML tasks in a single software module (with in Raspberry Pi) is challenging. This study will helpful towards the improvement in various practical applications areas relevant to small intelligent autonomous systems.

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Automatic Image Annotation Based on Particle Swarm Optimization and Support Vector Clustering

Cited by 7 publications

References 35 publications

Visual attention mechanism and support vector machine based automatic image annotation

Visual attention mechanism and support vector machine based automatic image annotation

Interpreting and semantically describing Chinese traditional brocade: Xilankapu

Automatic Image Annotation for Small and Ad hoc Intelligent Applications using Raspberry Pi

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