STRUCTURAL SIMILARITY MEASURE OF USERS PROFILES BASED ON A WEIGHTED BIPARTITE GRAPHS

The user profile is a very important tool in several fields such as recommendation systems, customization systems etc., it is used to narrow the number of data or results provided for a specific user, also to minimize the cost and the time of processing of multiple systems. Whatever the user profile model used, it’s updating and enrichment is a very essential step in the information research process in order to obtain more interesting and satisfactory results, which lead the information systems to develop several techniques aiming to enrich them based especially on similarity methods between user profiles. The similarity methods are used for several tasks such as the detection of duplicate profiles in online social network, also to answer the problem of cold start, and to predict users who can become friends as well as their future intentions, etc. In this paper, we propose a new approach to express the similarity between users profiles by developing a structural similarity measure to calculate the similarity between user profiles based on SimRank measure or similarity ,and the properties of bipartite graphs, in order to take advantage of the information provided by the relational structure between user profiles and their interests, our method is characterized by the similarity propagation between graph's nodes over iterations from source nodes to their successors, so our method finds profiles similar to the query profile, whether the links are direct or indirect between profiles.

Full-Reference Facial Image Quality Assessment and Identification by Two Proposed Measures

In this paper, the goal was to identify a person’s face in the acquired image by the proposed measures. We discuss the appearance of two types of noise together in an image. The acquired facial image quality was also assessed by two proposed measures, the histogram similarity measure and the histogram error mean measure. The histogram structural similarity measure is a previously described modified version of the information-theoretic structural similarity measure. It was merged with the structural similarity measure and the error mean measure, derived from the mean squared error, to get the proposed measures. The first proposed histogram similarity measure consists of merging histogram structural similarity with structural similarity measure, and the second proposed histogram error mean measure consists of merging histogram structural similarity with error mean measure. Finally, many algorithms for identification have recently been proposed to measure the similarity between two images. The results showed that the two proposed measures were better than existing methods. Different noises types (such as white Gaussian, speckle, and salt-and-pepper) are used with the proposed methods. Two facial image datasets were used in this paper. The AT&T database included color images of 92 x 112 pixels (px), and the Faculty of Industrial Engineering database included color images of 480 x 640 px. To evaluate performance and quantify the error, the structural similarity measure, histogram structural similarity, and error mean measure were considered. Noise ratios that depended on a peak signal-to-noise ratio were used in this experiment.

ASSESSMENT OF DIFFERENT IMAGE TRANSFORMATION METHODS ON DIWATA-1 SMI IMAGES USING STRUCTURAL SIMILARITY MEASURE

This paper aims to provide a qualitative assessment of different image transformation parameters as applied on images taken by the spaceborne multispectral imager (SMI) sensor installed in Diwata-1, the Philippines’ first Earth observation microsatellite, with the aim of determining the order of transformation that is sufficient for operationalization purposes. Images of the Palawan area were subjected to different image transformations by manual georeferencing using QGIS 3, and cloud masks generated and applied to remove the effects of clouds. The resulting images were then subjected to structural similarity (SSIM) tests using resampled and cloud masked Landsat 8 images of the same area to generate SSIM indices, which are then used as a quantitative means to assess the best performing transformation. The results of this study point to all transformed images having good SSIM ratings with their Landsat 8 counterparts, indicating that features shown in a Diwata-1 SMI image are structurally similar to the same features in a resampled Landsat 8 data. This implies that for Diwata-1 data processing operationalization purposes, higher order transformations, with the necessary effort to implement them, offer little advantage to lower order counterparts.