Multi-View Graph Fusion for Semi-Supervised Learning: Application to Image-Based Face Beauty Prediction

نویسندگان

چکیده

Facial Beauty Prediction (FBP) is an important visual recognition problem to evaluate the attractiveness of faces according human perception. Most existing FBP methods are based on supervised solutions using geometric or deep features. Semi-supervised learning for almost unexplored research area. In this work, we propose a graph-based semi-supervised method in which multiple graphs constructed find appropriate graph representation face images (with and without scores). The proposed combines both feature-based produce high-level instead single descriptor also improves discriminative ability score propagation methods. addition data graph, our approach fuses additional adaptively built predicted beauty values. Experimental results SCUTFBP-5500 facial dataset demonstrate superiority algorithm compared other state-of-the-art

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Semi-Supervised Learning Based Prediction of Musculoskeletal Disorder Risk

This study explores a semi-supervised classification approach using random forest as a base classifier to classify the low-back disorders (LBDs) risk associated with the industrial jobs. Semi-supervised classification approach uses unlabeled data together with the small number of labelled data to create a better classifier. The results obtained by the proposed approach are compared with those o...

متن کامل

Graph-based Semi-Supervised Learning Framework for Medical Image Retrieval

As low level features can not reflect the high level semantic in medical image search, in this paper, we propose an image retrieval algorithm to combine visual concept and local features by graph-based semi-supervised learning framework. More specific, we construct a graph model by distance between images, and add density similarity measure in the label propagation progress to get the membershi...

متن کامل

Graph-Based Semi-Supervised Learning

While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in ...

متن کامل

Application of three graph Laplacian based semi-supervised learning methods to protein function prediction problem

Protein function prediction is the important problem in modern biology. In this paper, the un-normalized, symmetric normalized, and random walk graph Laplacian based semi-supervised learning methods will be applied to the integrated network combined from multiple networks to predict the functions of all yeast proteins in these multiple networks. These multiple networks are network created from ...

متن کامل

Active graph based semi-supervised learning using image matching: Application to handwritten digit recognition

With the availability of large amounts of documents and multimedia content to be classified, the creation of new databases with labeled examples is an expensive task. Efficient supervised classifiers often require large training databases that are not always immediately available. Active learning approaches solve this issue by querying an expert to set a label to particular instances. In this p...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Algorithms

سال: 2022

ISSN: ['1999-4893']

DOI: https://doi.org/10.3390/a15060207