scholarly journals An Integrated approach to CBIR using multiple features and HSV Histogram

2016 ◽  
Vol 8 (5) ◽  
pp. 1946-1953
Author(s):  
Neelima N. ◽  
Sreenivasa Reddy E. ◽  
Kalpitha N.
2019 ◽  
Vol 8 (2) ◽  
pp. 5401-5405

Breast cancer is an alarming disease which takes millions of lives every year. In 2018, it was anticipated that 627,000 women died due to breast cancer – which is around 15% of all deaths caused due to different types of cancers among women. Currently, risk factors of breast cancer cannot be avoided, and early detection is the only way of survival. Automated detection of breast cancer with the help of image processing methods and machine learning algorithms helps in giving more accurate results and less human power. In the proposed system, multiple features are extracted using HSV histogram, LBP, GLCM, 2-D DWT. Support vector machine and LIBSVM classifiers are used for the classification of mammogram images if it’s benign or malign in nature. For classification, the INbreast dataset have been used which includes 115 cases containing 410 images. The dataset is divided into benign and malign category based upon BI-RAIDS scale. According to this partition we have 243 benign images and 100 malign images present in this dataset and a feature matrix of 595 features in total is generated for balanced and unbalanced datasets respectively and fed into SVM and LIBSVM to distinguish the data. The balanced datasets on LIBSVM gave best results with 92% accuracy, 84% sensitivity, 100% specificity and 91.30% F1 score followed by SVM which gave 75% accuracy, 73.61% sensitivity, 76.66% specificity and 75.8% F1 score.


2007 ◽  
Vol 6 (1) ◽  
pp. 185-186
Author(s):  
E COSENTINO ◽  
E RINALDI ◽  
D DEGLIESPOSTI ◽  
S BACCHELLI ◽  
D DESANCTIS ◽  
...  

1998 ◽  
Vol 14 (3) ◽  
pp. 202-210 ◽  
Author(s):  
Suzanne Skiffington ◽  
Ephrem Fernandez ◽  
Ken McFarland

This study extends previous attempts to assess emotion with single adjective descriptors, by examining semantic as well as cognitive, motivational, and intensity features of emotions. The focus was on seven negative emotions common to several emotion typologies: anger, fear, sadness, shame, pity, jealousy, and contempt. For each of these emotions, seven items were generated corresponding to cognitive appraisal about the self, cognitive appraisal about the environment, action tendency, action fantasy, synonym, antonym, and intensity range of the emotion, respectively. A pilot study established that 48 of the 49 items were linked predominantly to the specific emotions as predicted. The main data set comprising 700 subjects' ratings of relatedness between items and emotions was subjected to a series of factor analyses, which revealed that 44 of the 49 items loaded on the emotion constructs as predicted. A final factor analysis of these items uncovered seven factors accounting for 39% of the variance. These emergent factors corresponded to the hypothesized emotion constructs, with the exception of anger and fear, which were somewhat confounded. These findings lay the groundwork for the construction of an instrument to assess emotions multicomponentially.


2004 ◽  
Vol 49 (3) ◽  
pp. 337-338
Author(s):  
Robert T. Ammerman
Keyword(s):  

PsycCRITIQUES ◽  
2004 ◽  
Vol 49 (Supplement 14) ◽  
Author(s):  
Christine T. Chambers ◽  
Elizabeth A. Job
Keyword(s):  

2004 ◽  
Author(s):  
Jodi Abbott ◽  
Roxanne Felix ◽  
Karen Lee ◽  
Cynthia Smith

Sign in / Sign up

Export Citation Format

Share Document