## 5 Publicly Available Datasets and Benchmarks
### Places
Researchers utilized the bag-of-visual words framework before deep learning took the center stage and comprised of three different modules such as (i) feature extraction, (ii) code book learning, and (iii) coding processing. For any given image, the local features are extracted and are propagated to the code book learning module for extracting the visual words. This module uses k-Means clustering and extracts the k clusters by dividing the visual descriptors resembling the local features in terms of their Euclidean distances. Each cluster obtained represents a group of visual descriptors that share similar features whose center point is considered as the distinct visual word. This way all clusters containing the visual-words forms a code book. Finally, by incorporating all the learned features, the coding processing module predicts the contents of the entire image. In PFE, the codebook structure has crucial implications on the scene recognition performance. One possible limitation of codebook learning as discussed in [11] relies on the dictionary size meaning, the amount of visual words belonging to each category. The codebook learning becomes exorbitant considering a humungous amount of scene images that it is required to deal with. Additionally, it is inferred that the dimensionality of the derived codes increases manifold with the increase in the number of scene categories, resulting in more complexity and very slow inference processes. One such solution is to learn a codebook that is compact while simultaneously maintaining a higher recognition accuracy. This can be possible by getting rid of the correlated words using an indicator function. By using an automatic compact dictionary learning (ACDL) [86], the size of the codebook can be reduced. Besides this, constraints such as selectivity, sparsity, and discrimination may be incorporated to assure certain specific characteristics from the derived codes. The authors in [87] present a comprehensive analysis of the various codebook learning methods by emphasizing on the main characteristics of those methods. However, choosing an appropriate characteristic depends on several factors, which need further research and exploration. * The SPL pattern learning aims at increasing the scene recognition accuracy as some scenes may have certain specific spatial layouts. One such spatial layout utilizes the randomized spatial partitions [67] by considering both classification and optimal spatial partition as one single problem. Here, an input image is partitioned into a pool of several partitions, each representing a different size and shape. This is further transformed into a histogram based representation of features consisting of an ordered pair p(i, \(\theta_{j}\)) where i represents the level and \(\theta_{j}\) represents the partitioned patterns. Another spatial layout presented in [88] make use of class-specific spatial layouts that are obtained from spatial partitions based on the convolutional-feature maps. There have been several customized modules found in the literature that support various spatial structures, for example, randomized spatial pooling [69] and spatial pyramid pooling [89], to name a few. To examine the spatial structures that are more flexible, there have been several research efforts found in the literature. One such effort is the use of a randomized spatial pooling layer proposed in [90] that embodies appropriate spatial layout information to the CNN. * DRD is another way of independently extracting important regions or objects from the scenes. This is performed by using models such as deformable part based [90], and Object bank [70] to obtain the discriminative regions. However, to reduce the noisy features, important spatial pooling regions ISPR's are used in identifying and locating the discriminative regions. It is noted that ISPRs make use of part filters to preserve the quality of image regions.