A review of content-based video retrieval techniques for person identification

The rise of technology spurs the advancement in the surveillance field. Many commercial spaces reduced the patrol guard in favor of Closed-Circuit Television (CCTV) installation and even some countries already used surveillance drone which has greater mobility. In recent years, the CCTV Footage have also been used for crime investigation by law enforcement such as in Boston Bombing 2013 incident. However, this led us into producing huge unmanageable footage collection, the common issue of Big Data era. While there is more information to identify a potential suspect, the massive size of data needed to go over manually is a very laborious task. Therefore, some researchers proposed using Content-Based Video Retrieval (CBVR) method to enable to query a specific feature of an object or a human. Due to the limitations like visibility and quality of video footage, only certain features are selected for recognition based on Chicago Police Department guidelines. This paper presents the comprehensive reviews on CBVR techniques used for clothing, gender and ethnic recognition of the person of interest and how can it be applied in crime investigation. From the findings, the three recognition types can be combined to create a Content-Based Video Retrieval system for person identification

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification

Negativizing emotive coloronyms: A Kazakhstan-US Ethno-Psycholinguistic comparison

Neurotargeting prioritizes emotions in understanding collective unconscious and individual behavior. Comparative emotive linguistics reveals cross-cultural emotional expression variations. Despite extensive emotion research, gaps remain due to differing response norms. Psychology understands emotions well, but lacks universal classification, hindering linguistic description. Confusion between emotion and emotive obscures psychophysiological and verbal distinctions. Nonverbal emotives, reflecting emotions, require analysis of generation and expression mechanisms. This study examines color's role in conveying negative emotions in Kazakh writer A. Nurpeisov's "Blood and Sweat" and American writer T. Dreiser's "Trilogy of Desire." Authors use linguistic and nonverbal methods to portray emotions. Hypothesis: color as emotive state designation functions with "permissible-unacceptable" and "good-bad" evaluations, evident in shaping emotional reality perception. Analyzing coloristic negative emotives uncovers ethno-cultural metaphorical models, connecting emotive coloronyms with basic emotional concepts. Findings aid standardizing cognitive mechanisms for understanding mental experiences and comparative emotive linguistic terminology

Automatic detection of annular-granular patterns in melanoma \u3ci\u3ein situ\u3c/i\u3e dermoscopy images

Early detection of malignant melanoma greatly benefits patients, as the overall success is dependent on finding these melanomas before they reach the invasive stage. Dermoscopy is a non-invasive skin imaging technique that studies have shown can improve the diagnostic accuracy of dermatologists by as much as 30% over clinical examination. In this project machine vision and image analysis techniques are used to detect annular granular areas in dermoscopy images automatically. The proposed algorithm utilizes the luminance ratio between annular and granular areas within the darkest 30% of the lesion. All points whose luminance value are less than 30% of the histogram are considered for further processing. The method has used some preprocessing steps to remove the unwanted effect of luminance reflection, to extract hair and bubble from the lesion image and to enhance the contrast of the image. Then the lesion plane is searched to find the center and border of annular-granular areas. Statistical analysis has shown that the implemented algorithm has the highest 92 percent in correct detection of annular granular areas --Abstract, page iii