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Re-identification enables the tracking of the person taken from different disjoint camera aspects either from online or retrospectively for recognition of his or her visual appearance. Here a new method is proposed for person re-identification, taking into consideration the pose of the person as the primary factor, with multiple features being extracted from significant portions. Then angle-based pose priority has applied for matching and identification more robust to viewpoint. Their proposed method helps to reduce the number of images which are redundant in the training phase as well as the number of matching process in the test phase. The strength of the proposed method is demonstrated on three different benchmark databases containing more than 1000 person-images under variations in illumination, viewpoint and occlusion. The experimental results show that the proposed approach provides a higher recognition rates for all the issues of identification process. Finally, the results prove the superiority of the proposed method over other re-identification methods both in terms of visual and quantitative comparisons.

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This paper is the study of Fuzzy supra hyperconnected spaces (denoted by FSHS). After studying the characteristic of FSHS concerned with different concepts. The relationship between these concepts is investigated. The properties of FSHS are studied.

Background: Face annotation is the naming procedure to assign the correct name of a person who has emerged on an image. Objective: The main objective of this paper was to compare and evaluate six feature extraction techniques for face annotation under real-time challenging images and to find the best suitable feature for face annotation. Method: From literature review, it has been observed that Name Semantic Network (NSN) outperforms other annotation methods for various unconditioned images as well as ambiguous tags. However, the NSN’s performance can differ with various feature extraction techniques. Hence, its success is influenced by the feature extraction techniques that are used. Therefore, in this work, the NSN’s performance is experimented and evaluated with various feature extraction methods such as the Discrete Cosine Transform Local Binary Pattern (DCT-LBP), Discrete Fourier Transform Local Binary Pattern (DFT-LBP), Local Patterns of Gradients (LPOG), Gist, Local Order-constrained Gradient Orientations (LOGO) and Convolutional Neural Networks (CNNs) deep features. Results: Different feature extraction approaches demonstrate variations in performance with respect to a range of difficulties in face annotation using the Yahoo, LFW and IMFDB databases. The experimental results show that the deep feature method can achieve better recognition rate other than texture features. It confronts several issues in the presentation of a face in an image and produces better results. Conclusion: It is concluded that the CNNs deep feature is the best feature extraction technique that offers enhanced performance for face annotation.

Automated person re-identification is a key process in global distributed camera systems. This paper proposes a new feature, the Global and Local-Oriented Gabor Texture Histogram (GLOGTH), for person re-identification. GLOGTH is a combination of the local texture and global structure information of a given input image. This feature aims at representing the human appearance traits with low-dimensional feature extraction. The proposed feature extracts the texture information of input images based on the orientation of the weighted gradient from the global representation. In GLOGTH, the principal orientation is determined by the gradient of the pixels. Based on the principal orientation, the Gabor feature is extracted and imbues GLOGTH with the strong ability to express edge information, apart from making it robust to lighting variances. The experimental results acquired from the databases demonstrate that the proposed GLOGTH framework is capable of achieving notable improvements, in many cases reaching higher classification accuracy than traditional frameworks. HIGHLIGHTS• Novel feature extraction method for person re-identification.• Texture and shape based feature extraction.• Global and local feature extraction.• Performance analysis for different person re-identification issues. Poongothai E.; et al.

ABSTRACT:In this paper, the concepts of fuzzy supra pre σ-nowhere dense set, fuzzy supra pre σ-first category set and fuzzy supra pre σ-second category set in fuzzy topological spaces are introduced and studied. By means of fuzzy supra pre σ-nowhere dense sets, the concept of fuzzy supra pre σ-Baire space is defined and several characterizations of fuzzy supra pre σ-Baire spaces are studied. Several examples are given to illustrate the concepts introduced in this paper. KEYWORDS:Fuzzy supra pre dense set, fuzzy supra pre nowhere dense set, fuzzy supra pre F_σ-set, fuzzy supra pre G_δ-set, fuzzy supra pre σ-nowhere dense set, fuzzy supra pre σ-first category, fuzzy supra pre σ-second category, fuzzy supra pre σ-Baire space.