With the rise of deep learning algorithms nowadays, scene image representation methods have achieved a significant performance boost in classification. However, is still limited because images are mostly complex having higher intra-class dissimilarity and inter-class similarity problems. To deal with such problems, there been several proposed literature their advantages limitations. A detailed ...

The goal of multi-frame Super Resolution (SR) is to fuse multiple Low Resolution (LR) images to produce one High Resolution (HR) image. The major challenge of classic SR approaches is accurate motion estimation between the frames. To handle this challenge, fuzzy motion estimation method has been proposed that replaces value of each pixel using the weighted averaging all its neighboring pixels i...

Non-negative Matrix Factorization (NMF) is a part-based image representation method. It comes from the intuitive idea that entire face image can be constructed by combining several parts. In this paper, we propose a framework for face recognition by finding localized, part-based representations, denoted “Iterative weighted non-smooth non-negative matrix factorization” (IWNS-NMF). A new cost fun...

A novel signal representation using fuzzy mathematical morphology is developed. We take advantage of the optimum fuzzy fitting and the efficient implementation of morphological operators to extract geometric information from signals. The new representation provides results analogous to those given by the polynomial transform. Geometrical decomposition of a signal is achieved by windowing and ap...

The major approaches to image representation [l] in terms of its constituent regions may be divided into two broad categories: (1) those which specify the borders of the regions and (2) those which describe their interiors. Most of the approaches and the more interesting ones, belong to the latter category. This may be attributed to the increased dimensionality of information (regions, rather t...

Entity images provide significant visual information that helps the construction of knowledge representations. Most conventional methods learn knowledge representations solely from structured triples, ignoring rich visual information extracted from entity images. In this paper, we propose a novel Image-embodied Knowledge Representation Learning model, where knowledge representations are learned...

Image retargeting aims to resize an image to one with a prescribed aspect ratio. Simple scaling inevitably introduces unnatural geometric distortions on the important content of the image. In this paper, we propose a simple and yet effective method to resize an image, which preserves the geometry of the important content, using the Beltrami representation. Our algorithm allows users to interact...