Image Clustering using Color, Texture and Shape Features

Sleit, Azzam

doi:10.3837/tiis.2011.01.012

Cited by 16 publications

(9 citation statements)

References 10 publications

(9 reference statements)

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“…The color moment and Block Truncation Coding (BTC) are used to extract features and K-means clustering algorithm is applied to group 1,000 images into 10 clusters such as busses, dinosaurs, and flowers. Sleit et al [11] have utilized the color histogram, Gabor filters, and Fourier transformation for color, texture, and shape feature extraction, respectively to group images based on K-means clustering. The resultant image database includes four different groups which consist of dinosaurs, flowers, busses and elephants.…”

Section: Related Workmentioning

confidence: 99%

Situation-oriented clustering of sightseeing spot images using visual and tag information

Chen

Takama

2012

The 6th International Conference on Soft Computing and Intelligent Systems, and the 13th International Symposium on Advanced In

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Now a day, the tourists get used to take many photos in a journey and share these sightseeing spot images on album websites. The meaningful grouping of these images will become important and useful. In particular, sightseeing spot scenes are vary with different situations, such as weather conditions and seasons. Thus the categorization of different situations is expected to be beneficial for tourists to plan when to visit there. This paper proposes a hybrid approach which integrates contentbased image clustering with filtering based on tag information of image. Content-based image clustering categorizes sightseeing spot images into night, sunrise/sunset, cloudy, and shine situations based on color feature extraction from ROI (region of interest). By using geotag information, collected images can be limited to a reasonable boundary to eliminate outliers. Furthermore, by using the timestamp of images, the four situation categories constructed by content-based image clustering are further verified to increase the accuracy. Experimental results show that the hybrid approach of content-based image clustering and tag-based filtering is effective for obtaining clusters with high precision and recall.

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Section: Related Workmentioning

confidence: 99%

Situation-oriented clustering of sightseeing spot images using visual and tag information

Chen

Takama

2012

The 6th International Conference on Soft Computing and Intelligent Systems, and the 13th International Symposium on Advanced In

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“…It refers to the applications delivered as services over the Internet [1] [2] as well as hardware and systems software in the datacenters providing computing services. Cloud Computing is an attractive concept since it eliminates the provisioning planning requirements.…”

Section: Introductionmentioning

confidence: 99%

Cloud Computing Challenges with Emphasis on Amazon EC2 and Windows Azure

Sleit¹,

Misk²,

Badwan³

et al. 2013

IJCNC

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Cloud Computing has received much attention by the IT-Business world. As compared to the common computing platforms, cloud computing is more flexible in supporting real-time computation and is INTRODUCTIONCloud Computing is a newly proposed model for hosting and delivering computing services over the web. It refers to the applications delivered as services over the Internet [1] [2] as well as hardware and systems software in the datacenters providing computing services. Cloud Computing is an attractive concept since it eliminates the provisioning planning requirements. It also allows enterprises to start up with fewer resources to serve the existing needs and to grow gradually in response to increasing demands. For example, suppose that an enterprise has an immediate need and the required budget to deploy and maintain an internal application within a short period of time [3] [4]. Although IT hosting teams understand the requirement, deploying applications typically require extensive coordination between hardware, software, operation and support teams. The procurement phase for the required hardware and operating system setup may require several months. Application configuration and testing, building the required operation procedures and moving to production environment are major challenges and may not have the enterprise support and operations quality [5].The main challenges that an enterprise has in deploying an application are not in the application itself but in the prerequisites and procedures involved in providing the infrastructure required for deployment and maintenance. When cloud computing is implemented, the need for the application hosting team to depend on the hardware team is reduced, because hardware is encapsulated and provided by cloud computing data centers. If the dependencies on servers, load balancers, routers, and switches are eliminated, the application hosting team can focus solely on deploying the application in the cloud service provider of its choice, with business approval [6].

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“…Other type of solution is to use heuristic algorithms. Heuristic algorithms do not guarantee the optimal solutions but they can find optimal or near optimal solution in a reasonable time [4,21,22]. www.ijacsa.thesai.org …”

Section: Introductionmentioning

confidence: 99%

NMVSA Greedy Solution for Vertex Cover Problem

Eshtay¹,

Sleit²,

Sharieh³

2016

ijacsa

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Abstract-Minimum vertex cover (MVC) is a well-known NPComplete optimization problem. The importance of MVC in theory and practical comes from the wide range of its applications. This paper describes a polynomial time greedy algorithm to find near optimal solutions for MVC. The new algorithm NMVAS is a modification of already existed algorithm called MVAS which uses the same principle of selecting candidate from the neighborhood of the vertex with a modification in the selection procedure. A comparative study is conducted between the NMVAS and MVAS which shows that the proposed algorithm NMVSA provides better or equal results in the most cases of the underlying data sets which leads to a better average approximation ratio of NMVAS. NMVAS inherits the simplicity of the original algorithm.

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Image Clustering using Color, Texture and Shape Features

Cited by 16 publications

References 10 publications

Situation-oriented clustering of sightseeing spot images using visual and tag information

Situation-oriented clustering of sightseeing spot images using visual and tag information

Cloud Computing Challenges with Emphasis on Amazon EC2 and Windows Azure

NMVSA Greedy Solution for Vertex Cover Problem

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