This work presents a novel sparse ensemble learning scheme for concept detection in videos. The proposed ensemble first exploits a sparse non-negative matrix factorization (NMF) process to represent data instances in parts and partition the data space into localities, and then coordinates the individual classifiers in each locality for final classification. In the sparse NMF, data exemplars are projected to a set of locality bases, in which the non-negative superposition of basis images reconstructs the original exemplars. This additive combination ensures that each locality captures the characteristics of data exemplars in part, thus enabling the local classifiers to hold reasonable diversity in their own regions of expertise. More importantly, the sparse NMF ensures that an exemplar is projected to only a few bases (localities) with non-zero coefficients. The resultant ensemble model is, therefore, sparse, in the way that only a small number of efficient classifiers in the ensemble will fire on a testing sample. Extensive tests on the TRECVid 08 and 09 datasets show that the proposed ensemble learning achieves promising results and outperforms existing approaches. The proposed scheme is feature-independent, and can be applied in many other large scale pattern recognition problems besides visual concept detection.

Did you like this research project?

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

PROJECT TITLE :Efficient Secure Outsourcing of Large-Scale Sparse Linear Systems of Equations - 2018ABSTRACT:Solving large-scale sparse linear systems of equations (SLSEs) is one in all the foremost common and basic problems in
PROJECT TITLE :A Generative Model for Sparse Hyperparameter Determination - 2018ABSTRACT:Sparse autoencoder is an unsupervised feature extractor and has been widely used in the machine learning and knowledge mining community.
PROJECT TITLE :Sparse Representation Using Multidimensional Mixed-Norm Penalty With Application to Sound Field Decomposition - 2018ABSTRACT:A sparse representation methodology for multidimensional signals is proposed. In typically
PROJECT TITLE :Sparse Activity Detection for Massive Connectivity - 2018ABSTRACT:This Project considers the large connectivity application in that a giant number of devices communicate with a base-station (BS) during a sporadic
PROJECT TITLE :Rectified Gaussian Scale Mixtures and the Sparse Non-Negative Least Squares Problem - 2018ABSTRACT:In this Project, we develop a Bayesian evidence maximization framework to unravel the sparse non-negative least

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

Project Enquiry