You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Multimedia Internet of Things: A Comprehensive Survey 
Chapter Title:  B. FEATURE EXTRACTION 
1. Abstract of Cited Paper (BIB001):  The current explosion of data accelerated evolution of various content-based indexing techniques that allow to efficiently search in multimedia data such as images. However, index able features must be first extracted from the raw images before the indexing. This necessary step can be very time consuming for large datasets thus parallelization is desirable to speed the process up. In this paper, we experimentally compare two approaches to distribute the task among multiple machines: the Apache Hadoop and the Apache Storm projects. 
2. Abstract of Cited Paper (BIB002):  A multimedia semantic retrieval system based on hidden coherent feature groups (HCFGs) can support multimedia semantic retrieval on mobile applications. The system can capture the correlation between features and partition the original feature set into HCFGs, which have strong intragroup correlation while maintaining low intercorrelation. The authors present a novel, multimodel fusion scheme to effectively fuse the multimodel results and generate the final ranked retrieval results. In addition, to incorporate user interaction for effective retrieval, the proposed system also features a user feedback mechanism that helps refine the retrieval results. 
3. Abstract of Cited Paper (BIB003):  In this paper, a novel deep neural network (DNN)-driven feature learning method is proposed and applied to multi-view facial expression recognition (FER). In this method, scale invariant feature transform (SIFT) features corresponding to a set of landmark points are first extracted from each facial image. Then, a feature matrix consisting of the extracted SIFT feature vectors is used as input data and sent to a well-designed DNN model for learning optimal discriminative features for expression classification. The proposed DNN model employs several layers to characterize the corresponding relationship between the SIFT feature vectors and their corresponding high-level semantic information. By training the DNN model, we are able to learn a set of optimal features that are well suitable for classifying the facial expressions across different facial views. To evaluate the effectiveness of the proposed method, two nonfrontal facial expression databases, namely BU-3DFE and Multi-PIE, are respectively used to testify our method and the experimental results show that our algorithm outperforms the state-of-the-art methods. 
4. Abstract of Cited Paper (BIB004):  Assessing cognitive load during a learning phase is important, as it assists to understand the complexity of the learning task. It can help in balancing the cognitive load of postlearning and during the actual task. Here, we used electroencephalography (EEG) to assess cognitive load in multimedia learning task. EEG data were collected from 34 human participants at baseline and a multimedia learning state. The analysis was based on feature extraction and partial directed coherence (PDC). Results revealed that the EEG frequency bands and activated brain regions that contribute to cognitive load differed depending on the learning state. We concluded that cognitive load during multimedia learning can be assessed using feature extraction and measures of effective connectivity (PDC). 
5. Abstract of Cited Paper (BIB005):  By achieving breakthrough results on domains such as speech recognition, natural language processing, and computer vision, it is no surprise that deep neural networks are receiving a lot of attention these days. Specifically in the field of multimedia data analysis, there is a tremendous amount of multimedia big data that is being generated every day. Deep learning has the potential to overcome the issue of multimedia data having massive and heterogeneous characteristics that make it a challenge to store and analyze the data. This can be accomplished by allowing computers to easily and automatically extract features from unstructured data without the need to rely on human intervention. Although recent multimedia deep learning methods have achieved some remarkable results, deep learning challenges such as interpretability and generalization still make it difficult to be fit for critical decision-making tasks from fields such as medicine and defense.

Literature Review:
Multimedia devices generate a huge amount of data that comprise structured and unstructured data. The processing of such amount of unstructured data over an energy-constrained network requires the transmission of only meaningful information. An Intelligent feature extraction mechanism is required to identify and classify multimedia data with minimum energy consumption BIB002 , BIB005 . DL addresses the limitations of ML to process a huge amount of data BIB001 . DL offers proficient multimedia feature extraction models BIB006 , BIB004 . Extracting semantic from multimedia data could transform unstructured data into structured, indexed data for efficient processing BIB003 . Implementing DL over SDNs and adding semantics to multimedia data are yet to be explored for intelligent multimedia feature extractions.