## 5 Publicly Available Datasets and Benchmarks
### Places
Among all scene approaches to scene recognition, object correlation is more complex than object detection (identification of discriminative patches) is the first step for the analysis of subsequent correlations and require some real-time region proposal techniques. To this end, various probabilistic models have been developed to examine the connections among various objects and categories of scenes. Another disadvantage is that errors in detected objects or patches will have an impact on the subsequent correlations and hence, the recognition accuracy does not just relate to the object correlation but relies either on regional proposal techniques or the object detection model. Object correlation analysis achieves moderate accuracy in context to scene recognition task with slowest inference speeds due to computational overhead and serve as an optional choice for a purpose at hand. Hybrid deep models can achieve higher accuracies on massive scale datasets and combines the expressive powers of feature convolutions and the various methods to feature encoding such as CNN-DL [76], Hybrid CNNs [78], and VSAD [83], to name a few, that results in arduous training procedures, longer inference times and huge computational cost. Some tailored networks, for example, DAG-CNN [75] and FOSNET [77], that are end-to-end, attempt to simplify the training process by unifying extra information into the architecture. These hybrid deep models consume minimal computations and inference times to obtain a satisfactory recognition accuracy as they are favored with adequate computational resources. Limitations and Challenges of Scene Recognition Algorithms  
This section discusses the limitations and challenges of the scene recognition algorithms, namely, (i) global attribute descriptors (GADs), (ii) patch feature encoding (PFE), (iii) a spatial layout pattern learning (SPL), (iv) discriminative region detection (DRD), (v) object correlation analysis (OCA), and (vi) hybrid deep models (HDM) [11]. 1. In early 2000, scene extractions from images were mainly carried out using the GAD's. These descriptors utilized low-level image features such as the semantic typicality (this measure groups the natural real-world images in terms of their similarity into six different scene categories that include forests, coasts, rivers/lakes, plains, sky/clouds, and mountains, and categorizes a given image into one of those categories along side the nine local semantic concepts based on the frequency of image occurrence. Here, a archetypal categorical form of representation is learnt from each scene category and the \"typicality measure\" proposed is further evaluated (qualitatively and quantitatively) by incorporating a cross-validation on images containing 700 natural scenes. Furthermore, as typicality is a measure of uncertainty of predictions based on given annotations, and the nature of real-world images resembling an obscure nature, it is imperative to pay attention to the modeling of scene typicality after carrying out manual annotations [84]); a GIST (which is an abstract representation of a scene for activating the memory representations of different scene categories, such as sky, city, mountains, etc. ); a census-transform histogram (CENTRIST) [59] (a visual descriptor for identifying the scene categories or the topological places by encoding the structural properties in an image and by suppressing the detailed textual information. It is inferred that the model proved to be successful for both datasets related to scene categories and the topological places and has been noticeably faster); etc. These GAD's saw limited performance in scale for understanding the visual scene representations that are complex in nature. 2. To improve the performance, PFE gained prominence in the research community. It made use of the local features (aka. local visual descriptors), for example, histogram of oriented gradients, scale-invariant feature transform [85], bag-of-visual words, and local binary patterns, to name a few.