Regularized Phrase-based Topic Model for Automatic Question Classification with Domain-Agnostic Class Labels

2021 ◽  
pp. 1-1
Author(s):  
S Supraja ◽  
Andy W H Khong ◽  
Sivanagaraja Tatinati
Keyword(s):  
Topic Model ◽  
Question Classification ◽  
Class Labels

LambdaNet: A Fully Convolutional Architecture for Directional Change Detection

2020 ◽  
Vol 2020 (8) ◽  
pp. 114-1-114-7
Author(s):  
Bryan Blakeslee ◽  
Andreas Savakis
Keyword(s):  
Change Detection ◽  
Satellite Imagery ◽  
Classification Scheme ◽  
Deep Architecture ◽  
Binary Process ◽  
Directional Change ◽  
Class Labels ◽  
Image Pairs

Change detection in image pairs has traditionally been a binary process, reporting either “Change” or “No Change.” In this paper, we present LambdaNet, a novel deep architecture for performing pixel-level directional change detection based on a four class classification scheme. LambdaNet successfully incorporates the notion of “directional change” and identifies differences between two images as “Additive Change” when a new object appears, “Subtractive Change” when an object is removed, “Exchange” when different objects are present in the same location, and “No Change.” To obtain pixel annotated change maps for training, we generated directional change class labels for the Change Detection 2014 dataset. Our tests illustrate that LambdaNet would be suitable for situations where the type of change is unstructured, such as change detection scenarios in satellite imagery.

A Study on Bestseller Short Text Semantics Analysis Using Topic Model

2018 ◽  
Vol 15 ◽  
pp. 101-112
Author(s):  
So-Hyun Park ◽  
Ae-Rin Song ◽  
Young-Ho Park ◽  
Sun-Young Ihm
Keyword(s):  
Topic Model ◽  
Short Text

Twin labeled LDA: a supervised topic model for document classification

2020 ◽  
Vol 50 (12) ◽  
pp. 4602-4615
Author(s):  
Wei Wang ◽  
Bing Guo ◽  
Yan Shen ◽  
Han Yang ◽  
Yaosen Chen ◽  
...  
Keyword(s):  
Topic Model ◽  
Document Classification

scholarly journals COVIMOS: A Coastal Video Monitoring System

2017 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
I Made Oka Widyantara ◽  
I Made Dwi Asana Putra ◽  
Ida Bagus Putu Adnyana
Keyword(s):  
Monitoring System ◽  
Nearest Neighbor ◽  
Low Cost ◽  
Video Image ◽  
Video Monitoring ◽  
Reference Object ◽  
Self Organizing Map ◽  
K Nearest Neighbor ◽  
Ip Camera ◽  
Class Labels

This paper intends to explain the development of Coastal Video Monitoring System (CoViMoS) with the main characteristics including low-cost and easy implementation. CoViMoS characteristics have been realized using the device IP camera for video image acquisition, and development of software applications with the main features including detection of shoreline and it changes are automatically. This capability was based on segmentation and classification techniques based on data mining. Detection of shoreline is done by segmenting a video image of the beach, to get a cluster of objects, namely land, sea and sky, using Self Organizing Map (SOM) algorithms. The mechanism of classification is done using K-Nearest Neighbor (K-NN) algorithms to provide the class labels to objects that have been generated on the segmentation process. Furthermore, the classification of land used as a reference object in the detection of costline. Implementation CoViMoS system for monitoring systems in Cucukan Beach, Gianyar regency, have shown that the developed system is able to detect the shoreline and its changes automatically.

Sign in / Sign up

Export Citation Format

Share Document