Due to the progress of the digital image world and increasing numbers, preparing a system for image retrieval is essential. A content-based image retrieval system should find similar images to the image search by a user. In this paper, a novel content-based image retrieval system is proposed. Considering the importance of Texture in an image, we introduce a new feature as the histogram of the Texture difference in the equal edge orientation. Then, the expressed features are extracted from training images in the proposed system. Then these features are learned using a support vector machine. The proposed system is examined using the standard WANG database. The results show the efficiency of the proposed system in retrieving images compared to similar methods.

Content-based image retrieval is one of the interesting subjects in image processing and machine vision. In image retrieval systems, the query image is compared with images in the database to retrieve images containing similar content. Image comparison is done using features extracted from the query and database images. In this paper, the features are extracted based on the human visual system. Since the human visual system considers the Texture and the edge orientation in images for comparison, the colour difference histogram associated with the image’ s Texture and edge orientation is extracted as a feature. In this paper, the features are selected using the Shannon entropy criterion. The proposed method is tested using the Corel-5K and Corel-10K databases. The precision and recall criteria were used to evaluate the proposed system. The experimental results show the ability of the proposed system for more accurate retrieval rather than recently content-based image retrieval systems.

Face recognition is a challenging problem due to different illuminations, poses, facial expressions, and occlusions. In this paper, a new robust face recognition method is proposed based on the color and edge orientation difference histogram. Firstly, the color and edge orientation difference histogram is extracted using color, color difference, edge orientation, and edge orientation difference of the face image. Then the backward feature selection is employed in order to reduce the number of features. Finally, the Canberra measure is used to assess the similarity between the images. The color and edge orientation difference histogram shows the color and edge orientation difference between two neighboring pixels. This histogram is effective for face recognition due to the different skin colors and different edge orientations of the face image, which leads to a different light reflection. The proposed method is evaluated on the Yale and ORL face datasets. These datasets consist of gray-scale face images under different illuminations, poses, facial expressions, and occlusions. The recognition rate over the Yale and ORL datasets is achieved to be 100% and 98. 75%, respectively. The experimental results demonstrate that the proposed method outperforms the existing methods in face recognition.

A double ambiguity has been charged against Rawls’s difference principle (DP). Is it Maximin, Leximin, or something else? Usually, following A. Sen, scholars identify DP with the so-called Leximin. One argues here that one has to distinguish 1° the Leximin, 2° the Maximin (as rule of justice formally analogous to the maximin rule of decision), represented by the figure in L of the perfectly substitutable goods, and 3° the genuine DP. When the augmentation of inequality benefits the worse off, only Pareto-strong improvements are permitted. Leximin would also permit Pareto-weak improvements too (after the first maximum D), where only the richest improves: from (2, 3) to (2, 5), say. This is forbidden by DP. With two classes, unlike Maximin, DP has no curve of indifference and is always decisive, as Leximin is. For undecisive Rules of Justice, which admit indifferent curves, I propose to add a lexically secondary rule, to break ties. That move is able to clarify the links and the differences between on the one hand Maximin alone, with its typical indifference curves in L, and on the other hand, the DP properly understood and the Leximin, which both have no indifferent curves. With two classes of persons (best off/worse off), DP appears more egalitarian than Leximin, because it's secondary rule is MinIn (Minimization of Inequality). But the intuition behind the distinction is that it cannot possible “fair” that only the best off improves in a productive social cooperation.

Introduction: Content Based Image Retrieval (CBIR) is a method of image searching and retrieval in a database. In medical applications, CBIR is a tool used by physicians to compare the previous and current medical images associated with patients pathological conditions. As the volume of pictorial information stored in medical image databases is in progress, efficient image indexing and retrieval is increasingly becoming a necessity.Materials and Methods: This paper presents a new content based radiographic image retrieval approach based on histogram of pattern orientations, namely pattern orientation histogram (POH). POH represents the spatial distribution of five different pattern orientations: vertical, horizontal, diagonal down/left, diagonal down/right and non-orientation. In this method, a given image is first divided into image-blocks and the frequency of each type of pattern is determined in each image-block. Then, local pattern histograms for each of these image-blocks are computed.Results: The method was compared to two well known Texture-based image retrieval methods: Tamura and Edge histogram Descriptors (EHD) in MPEG-7 standard. Experimental results based on 10000 IRMA radiography image dataset, demonstrate that POH provides better precision and recall rates compared to Tamura and EHD. For some images, the recall and precision rates obtained by POH are, respectively, 48% and 18% better than the best of the two above mentioned methods.Discussion and Conclusion: Since we exploit the absolute location of the pattern in the image as well as its global composition, the proposed matching method can retrieve semantically similar medical images.