scholarly journals Modified JSEG algorithm for reducing over-segmentation problems in underwater coral reef images

2019 ◽  
Vol 9 (6) ◽  
pp. 5244
Author(s):  
Mohammad Sameer Aloun ◽  
Muhammad Suzuri Hitam ◽  
Wan NuralJawahir Hj Wan Yussof ◽  
Abdul Aziz K Abdul Hamid ◽  
Zainuddin Bachok
Keyword(s):  
Image Segmentation ◽  
Coral Reef ◽  
Texture Features ◽  
Color Histogram ◽  
Similarity Measurement ◽  
Segmentation Problem ◽  
Better Than ◽  
Observation Results ◽  
Color And Texture Features

<p>The original JSEG algorithm has proved to be very useful and robust in variety of image segmentation case studies.However, when it is applied into the underwater coral reef images, the original JSEG algorithm produces over-segementation problem, thus making this algorithm futile in such a situation. In this paper, an approach to reduce the over-segmentation problem occurred in the underwater coral reef image segmentation is presented. The approach works by replacing the color histogram computation in region merge stage of the original JSEG algorithm with the new computation of color and texture features in the similarity measurement. Based on the perceptual observation results of the test images, the proposed modified JSEG algorithm could automatically segment the regions better than the original JSEG algorithm.</p>

Content-Based Image Retrieval through Region Uniformly Partition

2012 ◽  
Vol 500 ◽  
pp. 471-474 ◽  
Author(s):  
Xiao Xiao ◽  
De Wen Zhuang ◽  
Shou Jue Wang
Keyword(s):  
Image Segmentation ◽  
Image Retrieval ◽  
Similarity Measure ◽  
Open Problem ◽  
Texture Features ◽  
Gabor Wavelet ◽  
Content Based Image Retrieval ◽  
Retrieval Performance ◽  
Color Features ◽  
Better Than

It has been demonstrated that accurate image segmentation is still an open problem. For avoiding this difficulties in content-based image retrieval, an region uniform partition approaching was proposed. Based on fusing regional color features using smooth slide histogram and texture features extracted using Gabor wavelet, we provided the corresponding similarity measure. The image retrieval performance on a subset of the COREL database are better than SIMPLIcity system showed the effectiveness of the proposed method.

Multi-Featured Content-Based Image Retrieval Using Color and Texture Features

2016 ◽  
Vol 850 ◽  
pp. 136-143 ◽  
Author(s):  
Mehmet Ayan ◽  
O. Ayhan Erdem ◽  
Hasan Şakir Bilge
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Texture Features ◽  
Color Histogram ◽  
Superior Performance ◽  
Content Based Image Retrieval ◽  
Occurrence Matrix ◽  
Color Moment ◽  
Color And Texture Features

Content-based image retrieval (CBIR) system becomes a hot topic in recent years. CBIR system is the retrieval of images based on visual features. CBIR system based on a single feature has a low performance. Therefore, in this paper a new content based image retrieval method using color and texture features is proposed to improve performance. In this method color histogram and color moment are used for color feature extraction and grey level co-occurrence matrix (GLCM) is used for texture feature extraction. Then all extracted features are integrated for image retrieval. Finally, color histogram, color moment, GLCM and proposed methods are tested respectively. As a result, it is observed that proposed method which integrates color and texture features gave better results than the other methods used independently. To demonstrate the proposed system is successful, it was compared with existing CBIR systems. The proposed method showed superior performance than other comparative systems.

The “How” of Animacy Effects in Episodic Memory

2015 ◽  
Vol 62 (6) ◽  
pp. 371-384 ◽  
Author(s):  
Patrick Bonin ◽  
Margaux Gelin ◽  
Betty Laroche ◽  
Alain Méot ◽  
Aurélia Bugaiska
Keyword(s):  
Memory Load ◽  
Recall Performance ◽  
Memory Task ◽  
Human Memory ◽  
Recall Rate ◽  
Interactive Imagery ◽  
Ultimate Explanation ◽  
Better Than ◽  
Observation Results

Abstract. Animates are better remembered than inanimates. According to the adaptive view of human memory ( Nairne, 2010 ; Nairne & Pandeirada, 2010a , 2010b ), this observation results from the fact that animates are more important for survival than inanimates. This ultimate explanation of animacy effects has to be complemented by proximate explanations. Moreover, animacy currently represents an uncontrolled word characteristic in most cognitive research ( VanArsdall, Nairne, Pandeirada, & Cogdill, 2015 ). In four studies, we therefore investigated the “how” of animacy effects. Study 1 revealed that words denoting animates were recalled better than those referring to inanimates in an intentional memory task. Study 2 revealed that adding a concurrent memory load when processing words for the animacy dimension did not impede the animacy effect on recall rates. Study 3A was an exact replication of Study 2 and Study 3B used a higher concurrent memory load. In these two follow-up studies, animacy effects on recall performance were again not altered by a concurrent memory load. Finally, Study 4 showed that using interactive imagery to encode animate and inanimate words did not alter the recall rate of animate words but did increase the recall of inanimate words. Taken together, the findings suggest that imagery processes contribute to these effects.

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