Semi-Supervised Image Classification Based on Local and Global Regression


The insufficiency of labeled samples may be a major drawback in automatic image annotation. However, unlabeled samples are readily available and abundant. Hence, semi-supervised learning ways, which utilize partly labeled samples and a large amount of unlabeled samples, have attracted increased attention in the field of image classification. Throughout the past decade, graph-based semi-supervised learning became one in all the foremost vital research areas in semi-supervised learning. In this letter, we tend to propose a novel and effective graph primarily based semi-supervised learning technique for image classification. The new method is predicated on native and international regression regularization. The native regression regularization adopts a group of native classification functions to preserve each native discriminative and geometrical data; whereas the global regression regularization preserves the global discriminative info and calculates the projection matrix for out-of-sample extrapolation. Intensive simulations primarily based on synthetic and real-world datasets verify the effectiveness of the proposed technique.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE :Unified Discriminative and Coherent Semi-Supervised Subspace Clustering - 2018ABSTRACT:The ubiquitous large, complex, and high dimensional datasets in computer vision and machine learning generates the matter of
PROJECT TITLE :Semi-Supervised Deep Learning Using Pseudo Labels for Hyperspectral Image Classification - 2018ABSTRACT:Deep learning has gained popularity in an exceedingly variety of computer vision tasks. Recently, it's also
PROJECT TITLE :Semi-Supervised Image-To-Video Adaptation For Video Action Recognition - 2017ABSTRACT:Human action recognition has been well explored in applications of pc vision. Many successful action recognition methods have
PROJECT TITLE : Adaptive ensembling of semi-supervised clustering solutions - 2017 ABSTRACT: Conventional semi-supervised clustering approaches have many shortcomings, such as (one) not absolutely utilizing all useful must-link
PROJECT TITLE :Semi-supervised Hierarchical Clustering for Semantic SAR Image AnnotationABSTRACT:During this paper, we tend to propose a semi-automated hierarchical clustering and classification framework for artificial aperture

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry