# Object/Scene Recognition Based on a Directional Pixel Voting Descriptor
## 2 Related Works
### State of the Art for Object Recognition  
In current state of the art in object recognition, deep-learning-based approaches, particularly Convolutional Neural Networks (CNNs), have demonstrated high performance and flexibility in terms of application domain. Models like ResNet, EfficientNet, and DenseNet have laid down the boundaries of object recognition accuracy and efficiency. In [23], the authors combine both object detection and image dehazing methods for real-time applications such as remote sensing, video surveillance, driverless automatic vehicles, etc. They presented an effective and efficient image dehazing method using transfer learning, which helps recognize objects in real time with more clarity and can automatically detect objects with a high recognition rate and lower probability of error. The trained model AODnet + YOLO v3 consistently outperforms non-joint and naive YOLO v3 techniques. Ref. [24] proposed a recurrent CNN (RCNN) for object recognition by incorporating recurrent connections into each convolutional layer. Though the input is static, the activities of RCNN units evolve over time so that the activity of each unit is modulated by the activities of its neighboring units. This property enhances the model's ability to integrate context information, which is important for object recognition. Like other recurrent neural networks, unfolding the RCNN through time can result in an arbitrarily deep network with a fixed number of parameters. Although both approaches allow high robustness in terms of detection, both algorithms were based on machine learning, and the accuracy under unknown objects, different than those in the training, limits the scope and performance. Furthermore, transfer learning techniques demonstrated high performance with pre-trained CNN models on large-scale datasets such as ImageNet for downstream object recognition tasks with limited labeled data. This approach allows for rapid development and deployment of object recognition systems in various domains. In [25], the use of transfer learning, via deep Convolutional Neural Networks (CNN), for the image classification problem within the context of X-ray baggage security screening was presented. The proposed algorithm achieved 98.92% detection accuracy, outperforming previous work, and further extended their evaluation to a multiple object classification task within this context. Additionally, in [26], multiple CNN architectures were proposed to improve the system performance. In this context, AlexNet, VGG16, and VGG19 are popular CNNs which allow transfer learning to fine-tune the pre-trained network parameters (VGG19) for image classification. Performance analysis demonstrated that the fine-tuned VGG19 architecture outperforms other CNN architectures and hybrid learning approaches for image classification tasks. While deep learning models achieved high performance in several domains, feature-based approaches like SIFT and SURF still find applications in scenarios with limited computational resources or when there are specific challenges such as small object detection or texture-rich environments. Feature-based methods such as Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF) are still relevant in object recognition tasks. These feature-based approaches offer distinct advantages, including robustness to variations in scale, rotation, and illumination. In [27], a new tactile-SIFT descriptor is proposed to extract features based on gradients in tactile images to represent objects, allowing the features to be invariant to object translation and rotation. The tactile-SIFT segments a tactile image into overlapping subpatches, each represented using a dn-dimensional gradient vector, similar to the classic SIFT descriptor. Tactile-SIFT descriptors obtained from multiple touches form a dictionary of k words, and the bag-of-words method is then used to identify objects.