Projects:2021s1-13291 Automated Content Moderation - Revision history

A1754148: /* Results */

2021-10-24T12:39:10Z

‎Results

A1754148: /* Results */

2021-10-24T11:04:54Z

‎Results

A1754148: /* Results */

2021-10-24T11:04:41Z

‎Results

A1751604 at 10:22, 24 October 2021

2021-10-24T10:22:51Z

A1751604 at 10:10, 24 October 2021

2021-10-24T10:10:40Z

A1747394 at 09:47, 24 October 2021

2021-10-24T09:47:43Z

A1747394 at 09:28, 24 October 2021

2021-10-24T09:28:45Z

A1747394 at 09:27, 24 October 2021

2021-10-24T09:27:55Z

A1747394 at 09:22, 24 October 2021

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A1747394 at 09:13, 24 October 2021

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 == Results ==
-'''Algorithm'''
+'''Classification'''
 Figure. 1 shows the image testing results. There are 6 images that have been tested, and each image is output with a classified label and the certainty of this label on the top. These testing images include all 3 types we trained, which are fire, not fire and candle. From Figure. 1, we can see every testing image has been given a correct classification based on the outputs of 7 nodes, even though some correct labels have 5/7 certainty or 6/7 certainty. For example, for the fire image with 5/7 certainty, we can know there are 2 nodes output a wrong label. On the other hand, there are several bright light images used for testing. The bright light images have quite similar features as fire, but they still get the correct classification with 7/7 certainty and 5/7 certainty.

@@ Line 77: / Line 77: @@
 == Results ==
-Algorithm
+'''Algorithm'''
 Figure. 1 shows the image testing results. There are 6 images that have been tested, and each image is output with a classified label and the certainty of this label on the top. These testing images include all 3 types we trained, which are fire, not fire and candle. From Figure. 1, we can see every testing image has been given a correct classification based on the outputs of 7 nodes, even though some correct labels have 5/7 certainty or 6/7 certainty. For example, for the fire image with 5/7 certainty, we can know there are 2 nodes output a wrong label. On the other hand, there are several bright light images used for testing. The bright light images have quite similar features as fire, but they still get the correct classification with 7/7 certainty and 5/7 certainty.

@@ Line 78: / Line 78: @@
 Algorithm
 Figure. 1 shows the image testing results. There are 6 images that have been tested, and each image is output with a classified label and the certainty of this label on the top. These testing images include all 3 types we trained, which are fire, not fire and candle. From Figure. 1, we can see every testing image has been given a correct classification based on the outputs of 7 nodes, even though some correct labels have 5/7 certainty or 6/7 certainty. For example, for the fire image with 5/7 certainty, we can know there are 2 nodes output a wrong label. On the other hand, there are several bright light images used for testing. The bright light images have quite similar features as fire, but they still get the correct classification with 7/7 certainty and 5/7 certainty.
 [[File:Figure. 1.png|thumb]]
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 The Robo 3T is used for database visualization.  The mongoDB database stores the results of image classification which is a dangerous fire scene into the folder named fire. The result is divided into two collections fs.chunk and fs.files. The fs.chunk collection stores the multiple small chunks of images and the fs.files files collection stores the metadata of the images and corresponds to chunks as figure 7 and 8 shows. These files in the database can be extracted again for further analysis.
 == Conclusion ==

← Older revision		Revision as of 10:22, 24 October 2021
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−	The Robo 3T is used for database visualization. ~~As figure 6 shows, the~~ mongoDB database stores the results of image classification which is a dangerous fire scene into the folder named fire. The result is divided into two collections fs.chunk and fs.files. The fs.chunk collection stores the multiple small chunks of images and the fs.files files collection stores the metadata of the images and corresponds to chunks as figure 7 and 8 shows. These files in the database can be extracted again for further analysis.	+	The Robo 3T is used for database visualization. The mongoDB database stores the results of image classification which is a dangerous fire scene into the folder named fire. The result is divided into two collections fs.chunk and fs.files. The fs.chunk collection stores the multiple small chunks of images and the fs.files files collection stores the metadata of the images and corresponds to chunks as figure 7 and 8 shows. These files in the database can be extracted again for further analysis.

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 ==== Storage ====
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 [[File:Table. 01.png|thumb]]
 [[File:Table. 02.png|thumb]]
 == Conclusion ==

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 == Method ==
 ==== Classification ====
-===== Datasets =====
+* Datasets
 The datasets used for training the models include 3 types of images: fire, not fire and candle. Part of the dataset provided for the training of this project was from 100 fire videos of Minneapolis Riot [published datasets]. Each video was captured at every 10 frames, and the images were manually classified into fire and non-fire categories. The rest of the images, taken from the internet, were sorted as a single type of candles in training datasets, giving the algorithm the ability to learn the characteristics of different types of fire. The total number of the training datasets is 2671. On the other hand, the final data that need to be classified in this project are videos collected from Snapchat, and materials that need to be stored are the classification results. Methods for data collection, video classification and data storage are explained below.
@@ Line 54: / Line 66: @@
 * Testing:
 The classification result of testing is based on 7 nodes. The logic used here is that if there are more than ⅓ frames captured from a video that are classified as fire for each node, the output of this node is defined as fire. The same logic goes for the candle and not fire labels. Take the one that occurs the most times out of the seven outputs as the final result. Test the classification result with different videos/images.
 == Results ==

@@ Line 2: / Line 2: @@
 [[Category:Final Year Projects]]
 [[Category:2018s1|106]]
-Open source intelligence refers to information or data that is legally derived from publicly available sources. Within the context of social media, many such items of open source data exist including images, videos and text. The use of this media however, carries a level of risk. Within the context of material uploaded by unknown individuals, exposure to content which is not safe for work can often result. Platforms, as a result, must employ algorithms to process media to detect explicit media in order to remove posts which violate the law and standards or expectations of the community. For the investigator, automatic classification often has the opposite requirement. Rather than removing extreme content, we wish to isolate for further study and possible action on behalf of law enforcement. In this project an analysis of current machine learning and signal processing methods will be conducted to create an effective system capable of extracting, filtering, flagging and storing content from the social media site - Snapchat.
+Open-source intelligence refers to information or data that is legally derived from publicly available sources. Within the context of social media, many such items of open-source data exist including images, videos, and text. The use of this media, however, carries a level of risk. Within the context of material uploaded by unknown individuals, exposure to content that is not safe for work can often result. Platforms, as a result, must employ algorithms to process media to detect explicit media in order to remove posts that violate the law and standards or expectations of the community. For the investigator, automatic classification often has the opposite requirement. Rather than removing extreme content, we wish to isolate for further study and possible action on behalf of law enforcement. In this project, an analysis of current machine learning and signal processing methods will be conducted to create an effective system capable of extracting, filtering, flagging, and storing content from the social media site - Snapchat.
 === Introduction ===
-Everyday people all around the word share their daily lives to people via their social media. In this day and age, a majority of the world’s population has access to the internet and social media. Due to this, many people are exposed to unfiltered content, where things can become viral in a matter of minutes whether they be illegal or not. The society needs to find a way to automate this regulation process. To take this one step future this project researches how to take this data, store and use it for law enforcement purposes. Having this in sight there are the following main questions:
+Every day people all around the world share their daily lives to people via their social media. In this day and age, a majority of the world’s population has access to the internet and social media. Due to this, many people are exposed to unfiltered content, where things can become viral in a matter of minutes whether they be illegal or not. Society needs to find a way to automate this regulation process. To take this one step future this project researches how to take this data, store and use it for law enforcement purposes. Having this insight there are the following main questions:
- - Can open-source media provide for a constant surveillance mechanism?
+* Can open-source media provide for a constant surveillance mechanism?
- - How to use social media to prevent/reprimand actions of individuals?
+* How to use social media to prevent/reprimand actions of individuals?
- - How to store large quantities of data for long periods of time whist preventing degradation of information.
+* How to store large quantities of data for long periods of time whilst preventing degradation of information.
 === Project team ===
@@ Line 18: / Line 18: @@
 * Matthew Sorell
 * Richard Matthews
 === Objectives ===
@@ Line 26: / Line 24: @@
 == Background ==
-Machine learning:
+==== Machine Learning ====
 Machine learning can be defined as the science of creating computer systems that can provide logical conclusions and process data about a changing world [2]. They use algorithms which are a set of processes that are performed in a step-by-step manner to divert attention to changing data sets [2]. They are able to discover irregularities and relationships between data sets. In machine learning, it is not the scientist's job to hard code rules about every situation a system can encounter, but it is more about giving the system enough information so it can detect what to look for and evolve [2]. A system should have the capacity to be able to recognise repeating patterns, trends and anomalies in a data set and categorize the initial – training – data provided [2]. It will then use this training provided to work on new sets of data [2]. Machine learning can be split into two streams, called supervised and unsupervised training [2]. In supervised training the programmer will give the system a set of initial data, specifying what sort of things the system must identify, then be given generic datasets for which it must apply itself [2]. Scientists can also use unsupervised training, in which the system is provided with an unlabeled data set and is programmed to identify the strongest categories or structuring principles (often called clusters) within it [2]. One would require training data with these variations to create classifiers that would work well on them [2]. Ideally, machine learning systems should be trained on diverse material to ensure that they are not overspecialized for a single context [1]. In this project the supervised learning mechanism will be used.
-Computer Vision:
+==== Computer Vision ====
 Computer vision is the analysis of digital visual images to understand both the objects that are depicted within them and the scenes from which they were constructed. Computer vision can involve detection of specific objects, segmentation (separation) of multiple objects within one scene, tracking these objects over time and space, three-dimensional reconstruction of the objects and/or scenes, and determination of the placement of the camera in the scene over time and/or space. Numerous methods are used to carry out these tasks, including color analysis, shadow and illumination analysis, geometrical analysis of curves and edges, and photogrammetry [2].
-Deep Learning:
+==== Deep Learning ====
 Deep learning is a relatively new method of machine learning. The reason deep learning is so popular is that deep learning can learn better and capture the features of the target image independently instead of manual extraction, which saves a lot of labour [3]. Deep learning has achieved great success in image recognition and computer vision [3].
-−
-Convolutional neural network:
+==== Convolutional neural network ====
 Convolutional neural network (CNN) is a neural network that contains multiple convolution calculations and a deep structure, so it belongs to deep learning and it is a branch of machine learning [4]. CNN was created around the 1980s [4]. After continuous development, it has been widely used in computer vision and artificial intelligence [4]. Many CNN models have repeatedly become the optimal algorithm in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [4]. At the same time, the large number of hidden layers of the CNN model gives it a more powerful ability to learn features. Therefore, the CNN model has a good performance in the classification of large-scale images. At present, Google, Microsoft and Facebook are developing and applying CNN on a larger scope [4]. The working principle of CNN is to use convolution between features and images [4]. Each picture has fixed natural features, so CNN can randomly take a piece of local data from the image as a training sample, and use the features learned from this small sample to filter the entire image, that is, combine these local features with the entire image [4]. The image is convolved to achieve the effect of learning the characteristics of each position of the image [4].
-Database storage:
+==== Database storage ====
 NoSQL database means non-relational SQL, it provides a mechanism for data storage and retrieval, which is not modeled in the table relationships used in relational databases [7]. NoSQL databases are widely used in big data and real-time network applications [7]. NoSQL is usually classified according to the data model, including Key–value store, Column store, Document stores and Graph database.

@@ Line 2: / Line 2: @@
 [[Category:Final Year Projects]]
 [[Category:2018s1|106]]
-Open source intelligence refers to information or data that is legally derived from publicly available sources. Within the context of social media, many such items of open source data exist including images, videos and text. The use of this media however, carries a level of risk. Within the context of material uploaded by unknown individuals, exposure to content which is not safe for work can often result. Platforms, as a result, must employ algorithms to process media to detect explicit media in order to remove posts which violate the law and standards or expectations of the community. For the investigator, automatic classification often has the opposite requirement. Rather than removing extreme content, we wish to isolate for further study and possible action on behalf of law enforcement. In this project an analysis of current machine learning and signal processing methods will be conducted to create an effective system capable of extracting, filtering, flagging and storing content from social media sites such as Snapchat, TikTok, Facebook and Reddit.
+Open source intelligence refers to information or data that is legally derived from publicly available sources. Within the context of social media, many such items of open source data exist including images, videos and text. The use of this media however, carries a level of risk. Within the context of material uploaded by unknown individuals, exposure to content which is not safe for work can often result. Platforms, as a result, must employ algorithms to process media to detect explicit media in order to remove posts which violate the law and standards or expectations of the community. For the investigator, automatic classification often has the opposite requirement. Rather than removing extreme content, we wish to isolate for further study and possible action on behalf of law enforcement. In this project an analysis of current machine learning and signal processing methods will be conducted to create an effective system capable of extracting, filtering, flagging and storing content from the social media site - Snapchat.
 === Introduction ===
@@ Line 12: / Line 12: @@
 === Project team ===
 ==== Project students ====
-* <Linyu Xu>
+* Sanjana Tanuku
-* <Sanjana Tanuku>
+* Siyu Wang
-* <Siyu Wang>
+* Linyu Xu
 ==== Supervisors ====
-* <Matthew Sorell>
+* Matthew Sorell
-* <Richard Matthews>
+* Richard Matthews