Paper Title
Non Linear Hog Model For Matching Driven Image Classification

In many fields image classification been focused only on bio medical datasets, which are often defined as datasets that can be easily classification using any methodologies. But there are two main reasons which limit the effectiveness of image classification. First, until the emergence of ImageNet dataset, there was almost no publicly available large-scale benchmark data for image classification. This is mostly because labels extractions are expensive to obtain. A key challenge is how to achieve efficiency in both feature extraction and classifier training without compromising performance. This paper is to show how we address this challenge using Image Net dataset.