Active Bayesian Mixture Learning for Image Modeling and Segmentation using Lowlevel Features

2006 ◽  
Author(s):  
Constantinos Constantinopoulos ◽  
Aristidis Likas
Keyword(s):  
Image Modeling ◽  
Bayesian Mixture

Accounting for Regressor-Error Dependencies in Educational Data: A Bayesian Mixture Approach

2014 ◽  
Author(s):  
Peter Ebbes ◽  
Ulf Bockenholt ◽  
Michel Wedel ◽  
Hyoryung Nam
Keyword(s):  
Bayesian Mixture

App2Vec: Context-Aware Application Usage Prediction

2021 ◽  
Vol 15 (6) ◽  
pp. 1-21
Author(s):  
Huandong Wang ◽  
Yong Li ◽  
Mu Du ◽  
Zhenhui Li ◽  
Depeng Jin
Keyword(s):  
Dirichlet Process ◽  
Service Providers ◽  
State Of The Art ◽  
Representation Learning ◽  
Context Aware ◽  
Challenging Problem ◽  
Performance Gap ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Spatio Temporal

Both app developers and service providers have strong motivations to understand when and where certain apps are used by users. However, it has been a challenging problem due to the highly skewed and noisy app usage data. Moreover, apps are regarded as independent items in existing studies, which fail to capture the hidden semantics in app usage traces. In this article, we propose App2Vec, a powerful representation learning model to learn the semantic embedding of apps with the consideration of spatio-temporal context. Based on the obtained semantic embeddings, we develop a probabilistic model based on the Bayesian mixture model and Dirichlet process to capture when , where , and what semantics of apps are used to predict the future usage. We evaluate our model using two different app usage datasets, which involve over 1.7 million users and 2,000+ apps. Evaluation results show that our proposed App2Vec algorithm outperforms the state-of-the-art algorithms in app usage prediction with a performance gap of over 17.0%.

Automatic snore sound extraction from sleep sound recordings via auditory image modeling

2016 ◽  
Vol 27 ◽  
pp. 7-14 ◽  
Author(s):  
Ryo Nonaka ◽  
Takahiro Emoto ◽  
Udantha R. Abeyratne ◽  
Osamu Jinnouchi ◽  
Ikuji Kawata ◽  
...  
Keyword(s):  
Image Modeling ◽  
Sound Recordings ◽  
Auditory Image

CONTENT-BASED IMAGE RETRIEVAL OF KAOU IMAGES BY RELAXATION MATCHING OF REGION FEATURES

2006 ◽  
Vol 14 (04) ◽  
pp. 509-523 ◽  
Author(s):  
KEISUKE KAMEYAMA ◽  
SOO-NYOUN KIM ◽  
MICHITERU SUZUKI ◽  
KAZUO TORAICHI ◽  
TAKASHI YAMAMOTO
Keyword(s):  
Image Retrieval ◽  
Chinese Character ◽  
Distance Functions ◽  
Content Based Image Retrieval ◽  
Historical Documents ◽  
Image Modeling ◽  
Weighted Sum ◽  
Old Japanese ◽  
Novel Method ◽  
Relaxation Matching

An improvement to the content-based image retrieval (CBIR) system for kaou images which has been developed by the authors group is introduced. Kaous are handwritten monograms found on old Japanese documents in a Chinese character-like shapes with artistic decorations. Kaous play an important role in the research of historical documents, which involve browsing and comparison of numerous samples. In this work, a novel method of kaou image modeling for CBIR is introduced, which incorporates the shade information of a closed kaou region in addition to the conventionally used contour characteristics. Dissimilarity of query and dictionary images were calculated as a weighted sum of elementary differences in the positions, contour shapes and colors of the component regions. These elementary differences were evaluated using relaxation matching and empirically defined distance functions. In the experiments, a set of 2455 kaou images were used. It was found that apparently similar kaou images could be retrieved by the proposed method, improving the retrieval quality. .

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