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from the various types of raw features associated with image popularity, which consequently affects the predictive accuracy of these models. In addition, these models require both time and skill to fine-tune their hyperparameters. This implies that developing a predictive model that can effectively handle multimodal information contributing to image popularity and accurately predict it is highly desired. Therefore, this dissertation proposes a multimodal deep learning system, called visual-social convolutional neural network (VSCNN), to address image popularity prediction on social media in an efficient way, that is, the proposed VSCNN system learn effective and high-level representations from various visual and social features that significantly influence image popularity and precisely predict it.
1.2 Motivation
In the real world, age estimation is a skill that we use in everyday life, and it also has an important influence on our daily social interactions. Several automatic age estimation systems are designed to estimate a person’s age from his/her face image, as the estimation of age by humans is not as easy as for determining other facial information (e.g., gender, identity, or expression). Although these systems have achieved promising results, the problem of age estimation is far from being solved. The major difficulty lies in how to design aging features that remain discriminative despite the significant variations in facial image appearance. This implies that addressing the automatic estimation of human age from face images is a well-established and challenging problem. In addition, the automatic human age estimation using
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facial image analysis has numerous potential real-world applications. These applications include:
(i) Security system access control: With an increasing number of crimes and terrorist threats, security control systems have become increasingly important in our daily lives. With the help of a monitoring camera, an automatic age estimation system can be used in the surveillance of bars as well as alcohol and cigarette vending machines to stop under-aged people from entering bars or wine stores and to prevent them from purchasing alcoholic drinks or cigarettes [44]. Age estimation can also be used to deny children access websites with unsuitable materials or restricted movies [45, 18]. In addition, age estimation can also play an important role in controlling money transfer fraud from ATMs by monitoring a specific age group that the police have found to be more prone to fraud [46].
(ii) Age-specific human-computer interaction: Individuals belonging to different age categories have various criteria and demands related to the way they interact with computers.
If an automated age estimator is used to determine the age of a computer user, both the computing environment and the user interface could be adjusted automatically to meet the needs of his/her age group [47, 48]. For example, interfaces based on colorful icons with appropriate illustrations can be activated when dealing with young kids, while interfaces based on icons with titles written in large fonts can be activated for older users.
(iii) Development of automatic age progression systems: Automatic age progression systems have the ability to simulate aging effects on new face images to predict how the person might look like in the future, or how he/she looked like in the past. Because automatic
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facial age estimation systems depend on their ability to comprehend and categorize changes in facial appearance because of aging, the methodology needed for this task could form the basis for designing automatic age progression systems [29]. In addition, age progression algorithms often require information related to the current age of an individual, and this emphasizes the essential role of facial age estimation systems in the development of automatic age progression systems.
(iv) Electronic customer relationship management (ECRM): ECRM uses modern internet-based technologies, such as chat rooms, blogs, emails, forums, and web sites, to efficiently manage the distinguished relationships with customers and communicate with them indi-vidually [46, 49]. As customers come from different age groups, they may have varied consumption patterns, preferences, and expectations for the products. Accordingly, automatic age estimation can be used by companies for monitoring market tendencies and customizing their products and services to satisfy the needs and desires of clients in various age groups.
The issue here is how substantive personal information from all customers’ age groups can be obtained and analyzed without infringing their privacy rights. However, a camera capturing pictures of the clients can collect demographic data by snapping the face images of clients and automatically estimating their age groups using an automated age estimation system. All of these can be done without violating the privacy of anyone.
(v) Biometrics: Age estimation is a kind of soft biometrics that provides additional information about users’ identity [50, 51]. It can be utilized to supplement the main biometric features, such as the face, fingerprints, iris, voice, and hand geometry, to enhance the performance and effectiveness of a hard (primary) biometrics system. For example, the
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system in real face recognition or identification applications often needs to recognize or identify faces after a gap that has lasted for many years (e.g., passport renewal or border security), that highlights the importance of age synthesis [52–54]. With the help of a dynamic aging model, the facial recognition system can dynamically fine-tune its parameters by taking into consideration the differences in face structure or skin texture during the aging process.
As a result, the efficiency of the system in the time gap could be substantially improved [55].
All the aforementioned recent application areas of automated age estimation imply the need for developing more precise age estimation systems.
In the online world, social media websites have been designed with the aim of facilitating and increasing social interactions among people on the internet. We can simply say that social media websites have altered the way we live and interact with. In addition, social media platforms have democratized the process of creating web content, allowing mere users to become creators and distributors of content. However, this has also led to massive growth in social content and has intensified the online competition for users’ attention. This is because the interactive behavior of Web users often makes some of the content published on social media more popular than others. Therefore, there is a growing research interest in modeling and predicting the popularity of social media content [56, 57]. Predicting the popularity of social media content, especially visual content such as images, can help us understand public interest and attention behind user interactions. It can also facilitate several practical applications, such as online advertising [58, 59], online marketing, network dimensioning (e.g., caching and replication) [56], content retrieval [60], and politics. For example, in the case of online advertising, advertisers would like to be able to predict the number of
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views that a specific advertisement might produce on a particular website. Thus, if the popularity count is directly related to advertisement profits (such as with advertisements shown with YouTube videos), profits may be fairly precisely estimated ahead of time if all parties know how many views the video is expected to receive. In addition, in online marketing, the popularity prediction of a given product on a marketing company website provides a great opportunity for the company to make more strategic decisions, such as better managing their resources and more effectively targeting their ads. In general, both the customer benefits from a more pleasurable experience and the company benefits from a monetary saving or gain. However, popularity prediction is not an easy task because of the difficulty in modeling and exploring the various factors that contribute to the popularity of social media content and, more specifically, image popularity. In addition, the popularity of different social media content co-evolves over time, and this evolution may be described by complex online interactions and information cascades that are difficult to predict at the microscopic level [61–63]. This implies that developing a popularity prediction system that can accurately predict the popularity of social media content is challenging and an active area of research.