# Inferring Emotion Tags from Object Images Using Convolutional Neural Network
## 1 Introduction  
Emotions represent the mental and psychological state of a human being and are a crucial element of human daily living behavior [1, 2]. They are a vital source to perceive, convey, and express a particular feeling and can stimulate the human mind 3000 times more than the rational thoughts [3]. With the rapid growth in digital media, emotion communication has increased through images [4, 5, 6, 7, 8],texts [9], speech [10], and audio-video contents [11]. According to [12], the contribution in the field of emotion classification is mainly based on image, speech, video, and text stimuli to extract useful features for testing various kinds of recognition algorithms. Recent studies have also focused on recognizing emotions from visual stimuli that encompass different objects [13; 14]. In real-life, the exposure of emotion occurrences can be either through digital media contents like visual stimuli or real-life objects [15]. However, it is tempting to ask what kind of information one can get from these visual stimuli to verify the image information interpretation. Likewise, it is crucial to know what type of emotions can be assigned to an image. There is no existing theory to model human emotions from images. However, according to the psychological and art theory [5], human emotions can be classified into specific types, such as dimensional and categorical. The dimensional theory represents the emotions as the coordinates of two or three-dimensional space, i.e., valence-arousal or valence-arousal-dominance [16]. On the other hand, categorical theory classifies the emotions into well-suited classes, e.g., positive and negative categories [6]. [PERSON] et al. [17] introduced five essential emotion labels based on facial expression, which include happiness, disgust, anger, fear, and sadness, without any discrete emotion classification. The same emotion categories have been utilized in various studies [18; 19; 20]. The authors in [21] presented eight emotion categories to study human emotion perception, where happiness emotion is split into four positive emotions, namely amusement, excitement, awe, and contentment, while negative sentiments are further categorized as anger, fear, disgust, and sad emotions [14; 22]. In [3], nine emotion categories were introduced with the addition of boredom emotion for visual emotion analysis. An image can be either interpreted in terms of explicit or implicit information. The explicit information is object recognition, whereas implicit information is known as emotion perception, which is subjective and more abstract than explicit information [23]. Moreover, there are two major types of emotions, including perceived emotion and induced emotions, as reported by [PERSON] [24]. The perceived emotion refers to what a recipient/viewer thinks that the sender wants to express. On the other hand, induced emotion refers to what a person perceives when subjected to some visual stimuli, such as an image or video. Thus, inferring induced emotions is somewhat difficult than perceived emotions [3]. Various researchers have devoted considerable efforts towards image emotion analysis using visual stimuli (i.e., images) incorporating machine learning [13; 25; 26; 27] and deep learning [14; 23; 28] approaches. These studies aim to focus on modeling emotions based on several learning algorithms and to extract useful features for image emotion analysis. However, the problem of inferring emotion tags with hand-crafted visual features is very challenging as these features at times are not able to interpret a specific emotion from an image with high accuracy. It is because these features are based on human assumptions and observations and may lack some essential factors of modeling and image [14]. As a result, deep learning with a convolutional neural network (CNN) has shown greater success with automatically learned features in affective computing [29].