scholarly journals Distilled Collections from Textual Image Queries

2015 ◽  
Vol 34 (2) ◽  
pp. 131-142 ◽  
Author(s):  
Hadar Averbuch-Elor ◽  
Yunhai Wang ◽  
Yiming Qian ◽  
Minglun Gong ◽  
Johannes Kopf ◽  
...  
Keyword(s):  
Textual Image

Textual image compression: two-stage lossy/lossless encoding of textual images

1994 ◽  
Vol 82 (6) ◽  
pp. 878-888 ◽  
Author(s):  
I.H. Witten ◽  
T.C. Bell ◽  
H. Emberson ◽  
S. Inglis ◽  
A. Moffat
Keyword(s):  
Image Compression ◽  
Two Stage ◽  
Textual Image ◽  
Textual Image Compression

Textual image compression

2003 ◽  
Author(s):  
I.H. Witten ◽  
T.C. Bell ◽  
M.-E. Harrison ◽  
M.L. James ◽  
A. Moffat
Keyword(s):  
Image Compression ◽  
Textual Image ◽  
Textual Image Compression

scholarly journals Remote Sensing Textual Image Classification based on Ensemble Learning

2016 ◽  
Vol 8 (12) ◽  
pp. 21-29 ◽  
Author(s):  
Ye zhiwei ◽  
◽  
Yang Juan ◽  
Zhang Xu ◽  
Hu Zhengbing
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Ensemble Learning ◽  
Textual Image

Thwarting Spam on Facebook

2018 ◽  
pp. 51-70
Author(s):  
Arti Jain ◽  
Reetika Gairola ◽  
Shikha Jain ◽  
Anuja Arora
Keyword(s):  
Machine Learning ◽  
Online Social Networks ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Testing Dataset ◽  
Learning Techniques ◽  
Textual Image ◽  
Combined Feature ◽  
Entire Dataset ◽  
F Measure

Spam on the online social networks (OSNs) is evolving as a prominent problem for the users of these networks. Spammers often use certain techniques to deceive the OSN users for their own benefit. Facebook, one of the leading OSNs, is experiencing such crucial problems at an alarming rate. This chapter presents a methodology to segregate spam from legitimate posts using machine learning techniques: naïve Bayes (NB), support vector machine (SVM), and random forest (RF). The textual, image, and video features are used together, which wasn't considered by the earlier researchers. Then, 1.5 million posts and comments are extracted from archival and real-time Facebook data, which is then pre-processed using RStudio. A total of 30 features are identified, out of which 10 are the best informative for identification of spam vs. ham posts. The entire dataset is shuffled and divided into three ratios, out of which 80:20 ratio of training and testing dataset provides the best result. Also, RF classifier outperforms NB and SVM by achieving overall F-measure 89.4% on the combined feature set.

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