Image Segmentation Techniques: A Survey

Khan, W. A.

doi:10.12720/joig.1.4.166-170

Cited by 128 publications

(79 citation statements)

References 23 publications

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“…Global (i.e., Whole Fish) features of image No (6, 8, and 14), (9,12, and 16), (11 and 13), (20, 23, and 29), (3 and 27) and (10 and 26) are almost equal while the fish images belong to different species. On the other hand, if the local features of the head, abdomen and tail segments of those fish images are compared with the local features of segments of their corresponding fish images then major difference can be seen.…”

Section: Comparison Of Global and Local Featuresmentioning

confidence: 98%

“…In [11], the survey on different color image segmentation techniques suggests that histogram thresholding method is simple, robust and universally adopted in many situations. In [12], the survey on image segmentation techniques concludes that hybrid solution of more than one technique is the best approach for solving image segmentation.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Fish Image Segmentation Algorithm (FISA) for Improving the Performance of Image Retrieval System

Baloch¹,

Ali²,

Gul³

et al. 2017

ijacsa

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Abstract-The image features (local, global) pay vital role in image retrieval system. The effectiveness of these image features depends on the application domain, i.e., in some domains the global features generate better results while in others the local features give good results. Different species of fishes have different color, texture, and shape features in their body parts (head, abdomen, and tail). Previously most of the work, in fish image domain has been done using global features. This work claims that fish image retrieval system using local features can generate better results as compared to global features. This is because of the fact that fish image has different features in its body parts. In this research, a fish image segmentation algorithm is proposed to extract fish object from its background and then separate fish object into three distinguished body parts, i.e. head, abdomen, and tail. The proposed algorithm was tested on a subset of "QUT_fish_data" data set containing 369 fishes of various sizes of 30 species. The experimental results showed an accuracy of 87.5% on fish image segmentation and demonstrated the effectiveness of local features over global features.

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Section: Comparison Of Global and Local Featuresmentioning

confidence: 98%

Section: Literature Reviewmentioning

confidence: 99%

Fish Image Segmentation Algorithm (FISA) for Improving the Performance of Image Retrieval System

Baloch¹,

Ali²,

Gul³

et al. 2017

ijacsa

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show abstract

“…The k-mean clustering is regularly utilized for deciding the association of the information. Clustering is the undertaking of collection an arrangement of articles such that items in a similar gathering are more comparative .This unsupervised bunching approach has a solid proclivity to get caught into nearby minimal while creating an ideal arrangement and subsequently it makes grouping completely reliant on the essential group focuses appropriation [12]. …”

Section: C) Clustering Based Segmentationmentioning

confidence: 99%

Computational Approach to Image Segmentation Analysis

Gourav¹,

Sharma²,

Singh³

2017

IJMECS

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Abstract-Image division refers to the way toward dividing an advanced picture into various portions. Image division says to a parcel of an image into various divisions that are homogeneous or comparable. The objective of the division is to Simplify or potentially changes the portrayal of an image into something that is more important and simpler to dissect. Advancement of precise image division different image division strategies is utilized to take care of a particular issue. The motivation behind this survey is to give an overview of various image division methods. These methods are sorted into four sorts: an) Edge based division b) Threshold Segmentation c) Clustering-based division D) Region-based division. This survey tended to different image division methods, their correlation and presents the issues identified with those procedures.

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“…After the analysis of many image segmentation methods, it is noticed that a hybrid solution for image segmentation composes of two or more methods is being the best way to solve the image segmentation problem. [14] …”

Section: Related Workmentioning

confidence: 99%