Regularization on Augmented Data to Diversify Sparse Representation for Robust Image Classification


The process of image classification is an essential part of today's computer vision systems. Due to the robustness of the sparse representation-based classification method, it has garnered a lot of attention in recent years. However, regularization and data augmentation are both powerful techniques, but they are currently being used in isolation when it comes to the optimization of sparse learning systems. We have reason to believe that the combination of data augmentation and regularization can bring about a significant improvement in the accuracy of image classification. Regularization on Augmented Data (READ) is a novel framework that we propose in this article. It generates diversification in the data by making use of generic augmentation techniques in order to implement robust sparse representation-based image classification. When the training data are augmented, READ applies a separate regularizer, in this case either l1 or l2, to the augmented training data in addition to the original data. This ensures that regularization and data augmentation are both utilized and improved at the same time. We present a comprehensive theoretical analysis on how to optimize the sparse representation by both l1 -norm and l2 -norm with the generic data augmentation, and we demonstrate the effectiveness of our approach through a number of in-depth experiments. When it comes to the use of deep features, the results that READ achieved on a variety of facial and object datasets demonstrate that it outperforms many other state-of-the-art methods.

Did you like this research project?

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

PROJECT TITLE : Robust Fuzzy Learning for Partially Overlapping Channels Allocation in UAV Communication Networks ABSTRACT: The emerging cellular-enabled unmanned aerial vehicle (UAV) communication paradigm poses significant challenges
PROJECT TITLE : Server-Aided Fine-Grained Access Control Mechanism with Robust Revocation in Cloud Computing ABSTRACT: In a wide variety of cloud computing applications, attribute based encryption, also known as ABE, makes it
PROJECT TITLE : Robust H∞ Network Observer-Based Attack-Tolerant Path Tracking Control of Autonomous Ground Vehicle ABSTRACT: Under the influence of external disturbance, measurement noise, and actuator/sensor attack signals,
PROJECT TITLE : Robust Localization System using Vector Combination in Wireless Sensor Networks ABSTRACT: In this paper, we propose a localization system that is based on vectors and that takes into account both distance and
PROJECT TITLE : Robust Variational Learning for Multiclass Kernel Models With Stein Refinement ABSTRACT: The ability of kernel-based models to generalize well is impressive, but the vast majority of them, including the SVM, are

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

Project Enquiry