Opinion mining is considered as a subfield of natural language processing, information retrieval and text mining. Opinion mining is the process of extracting human thoughts and perceptions from unstructured texts, which with regard to the emergence of online social media and mass volume of users’ comments, has become to a useful, attractive and also challenging issue. There are varieties of researches with different trends and approaches in this area, but the lack of a comprehensive study to investigate them from all aspects is tangible. In this paper we represent a complete, multilateral and systematic review of opinion mining and sentiment analysis to classify available methods and compare their advantages and drawbacks, in order to have better understanding of available challenges and solutions to clarify the future direction. For this purpose, we present a proper framework of opinion mining accompanying with its steps and levels and then we completely monitor, classify, summarize and compare proposed techniques for aspect extraction, opinion classification, summary production and evaluation, based on the major validated scientific works. In order to have a better comparison, we also propose some factors in each category, which help to have a better understanding of advantages and disadvantages of different methods.

A survey on classification techniques for opinion mining and sentiment analysis

A review of state-of-the-art techniques for abnormal human activity recognition

Human Action Recognition (HAR) involves human activity monitoring task in different areas of medical, education, entertainment, visual surveillance, video retrieval, as well as abnormal activity identification, to name a few. Due to an increase in the usage of cameras, automated systems are in demand for the classification of such activities using computationally intelligent techniques such as Machine Learning (ML) and Deep Learning (DL). In this survey, we have discussed various ML and DL techniques for HAR for the years 2011–2019. The paper discusses the characteristics of public datasets used for HAR. It also presents a survey of various action recognition techniques along with the HAR applications namely, content-based video summarization, human–computer interaction, education, healthcare, video surveillance, abnormal activity detection, sports, and entertainment. The advantages and disadvantages of action representation, dimensionality reduction, and action analysis methods are also provided. The paper discusses challenges and future directions for HAR.

A survey on video-based Human Action Recognition: recent updates, datasets, challenges, and applications

Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence/machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2019. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 176 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

We present a system for collecting and analyzing information on street lighting infrastructure. We develop a car-mounted sensor platform that enables collection and logging of data on street lights during night-time drive-bys. We address several signal processing problems that are key to mapping street illumination levels, identifying street lamps, estimating their heights, and geotagging them. Specifically, we highlight an image recognition algorithm to identify street lamps from the video data collected by the sensor platform and its subsequent use in estimating the heights of street lamps. We also outline a framework to improve vehicle location estimates by combining sensor observations in an extended Kalman filter framework. Our eventual goal is to develop a semi-live virtual 3-D street lighting model at urban scale that enables citizens and decision makers to assess and optimize performance of nighttime street lighting.

Urban Street Lighting Infrastructure Monitoring Using a Mobile Sensor Platform

Human fall detection using machine vision techniques on RGB–D images

Automatic text detection and recognition (end-to-end text recognition) in real-life images are the main elements of many applications including blind and low vision assistance systems and self-driving cars. However, it is challenging to detect curved and vertical texts due to their color bleeding, font size variation, and complicated background. In this paper, a convolutional neural network-based pipeline is introduced to obtain high-level visual features and improve text detection and recognition efficiency. A pre-trained ResNet-50 network on ImageNet and SynthText for extracting low-level visual features was used in this study. Moreover, new improved ReLU layer (new.i.ReLU) blocks are used with a varied receptive field with a strong ability to detect text components even on curved surfaces in the proposed structure. A new improved inception layer (new.i.inception layers) can obtain broadly varying-sized text more effectively than a linear chain of convolution layer. Also, we have proposed a pipeline framework for character recognition that is robust to irregular (curve and vertical) text. First, we introduced a novel algorithm for encoding pixel’s value to a new one called local word directional pattern (LWDP) that highlights the texture of the characters. Then, the output of LWDP was presented as an input image in the text recognition process. The experiments on standard benchmarks, including ICDAR 2013, ICDAR 2015, and ICDAR 2019 datasets, illustrated the superiority of the proposed architecture over prior works.

A novel pipeline framework for multi oriented scene text image detection and recognition

Data warehouses are subject oriented, consolidated, integrated, and time variant repository of possibly heterogeneous data. A data warehouse is used to response to on-line analytical queries over the millions records of data in an acceptable time. Since a data warehouse often has millions of records of data, it is an important challenge how we can reduce the time of on-line analytical processing. One of the most important issues which address this problem is the view materialization. Each sub-query results an intermediate table, called virtual view, which is used to find final result of the analytical query. These virtual views often are commonly used to response to several analytical queries. We can materialize such views to prevent multiple redundant computations and thus lead to reduction in response time of queries. The constraint of storage memory on one hand, and the maintenance cost of materialized views when the source data are updated on the other hand, cause that it is impossible to materialize all or even large part of views. Therefore, selection of a proper set of views to materialization plays a major role in performance. There are many methods of view selection to materialization which uses different techniques and frameworks to select optimal set of views to materialization. In this paper, we present a new efficient method to conduct selecting proper set of views to materialization using a frequent itemset mining approach. In our algorithm, the set of given queries is transformed to a transaction database where a transaction corresponds to a query and items of a transaction are the original query's predicates. Our performance study showed that this algorithm outperformed substantially the best former algorithms.

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Materialized View Selection for a Data Warehouse Using Frequent Itemset Mining

Generally, a spam image is an unsolicited message electronically sent to a wide group of arbitrary addresses. Due to attractiveness and more difficult detection, spam images are the most complicated type of spam. One of the ways to encounter the spam images is an optical character recognition, OCR, method. In this paper, the proposed enhanced HOG feature extraction method has been used so that the optical character recognition system of spam has been enhanced by using the HOG feature extraction method in such a way to be both resistant against the character variations on scale and translation and to be computationally cost-effective. For these purposes, two steps of the cropped image and input image size normalization have been added to pre-processing stages. Support vector machine, SVM, was employed for classification. Two heuristic modifications including thickening of the thin characters in the pre-processing stage and non-discrimination in detecting the uppercase and lowercase letters with the same shapes in the classification stage have been also proposed to increase the system recognition accuracy. In the first heuristic modification, when all pixels of the output image are empty (the character is eliminated), the original image was made thicker by one layer. In the second modification, when recognizing the letters, no differentiation was considered between the uppercase and lowercase letters with the same shapes. An average recognition accuracy of the modified HOG method with two heuristic modifications equals 91.61% on Char74K database. Then, an optimum threshold for classification was investigated by ROC curve. The optimal cutoff point was 0.736 with the highest average accuracy, 94.20%, and AUC, area under curve, for ROC and precision–recall, PR, curves were 0.96 and 0.73, respectively. The proposed method was also examined on ICDAR2003 database, and the average accuracy and its optimum using ROC curve were 82.73% and 86.01%, respectively. These results of recognition accuracy and AUC for ROC and PR curve showed an outstanding enhancement in comparison with the best recognition rate of the previous methods.

Vahid Ghods

Papers

Human fall detection using machine vision techniques on RGB–D images

A novel pipeline framework for multi oriented scene text image detection and recognition

Materialized View Selection for a Data Warehouse Using Frequent Itemset Mining

An efficient character recognition method using enhanced HOG for spam image detection

Decision fusion of horizontal and vertical trajectories for recognition of online Farsi subwords