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: Feature Encodings and Poolings for Action and Event Recognition: A Comprehensive Survey 
Chapter Title:  Locality-Constrained Linear Encoding 
1. Abstract of Cited Paper (BIB001):  Recently SVMs using spatial pyramid matching (SPM) kernel have been highly successful in image classification. Despite its popularity, these nonlinear SVMs have a complexity O(n2 ~ n3) in training and O(n) in testing, where n is the training size, implying that it is nontrivial to scaleup the algorithms to handle more than thousands of training images. In this paper we develop an extension of the SPM method, by generalizing vector quantization to sparse coding followed by multi-scale spatial max pooling, and propose a linear SPM kernel based on SIFT sparse codes. This new approach remarkably reduces the complexity of SVMs to O(n) in training and a constant in testing. In a number of image categorization experiments, we find that, in terms of classification accuracy, the suggested linear SPM based on sparse coding of SIFT descriptors always significantly outperforms the linear SPM kernel on histograms, and is even better than the nonlinear SPM kernels, leading to state-of-the-art performance on several benchmarks by using a single type of descriptors. 
2. Abstract of Cited Paper (BIB002):  The traditional SPM approach based on bag-of-features (BoF) requires nonlinear classifiers to achieve good image classification performance. This paper presents a simple but effective coding scheme called Locality-constrained Linear Coding (LLC) in place of the VQ coding in traditional SPM. LLC utilizes the locality constraints to project each descriptor into its local-coordinate system, and the projected coordinates are integrated by max pooling to generate the final representation. With linear classifier, the proposed approach performs remarkably better than the traditional nonlinear SPM, achieving state-of-the-art performance on several benchmarks. Compared with the sparse coding strategy [22], the objective function used by LLC has an analytical solution. In addition, the paper proposes a fast approximated LLC method by first performing a K-nearest-neighbor search and then solving a constrained least square fitting problem, bearing computational complexity of O(M + K2). Hence even with very large codebooks, our system can still process multiple frames per second. This efficiency significantly adds to the practical values of LLC for real applications.

Literature Review:
Wang et al. BIB003 pointed out that in order to achieve desirable performance in classification tasks, the most common combinational strategies adopt either linear encodings plus nonlinear classifiers, such as BOV BIB001 plus χ 2 -SVM, or nonlinear encodings plus linear classifiers, such as ScSPM BIB002 plus linear SVM. Usually, either the nonlinear encodings or the nonlinear classifiers are computationally expensive. As a result, Wang et al. proposed a fast linear encoding approach, called Locality-constrained Linear Coding (LLC) BIB003 for real-time classification applications. Suppose X = x i 1 ≤ i ≤ n, x i ∈ R l is a set of n local l dimensional feature vectors extracted from an image, and D = {d 1 , d 2 , · · · , d m } ⊂ R l×m is the encoding dictionary with m words. Then, the process of the proposed fast linear LLC encoding works as follows: (1) For each x i , find its k nearest neighbors from the dictionary D by using k-NN method, denoted as , where c i 0 = k and 1 T c i = 1. The authors showed that the promising LLC approach cannot only improve encoding velocity, but also classification performance even with linear classifier than several compared approaches.