Scene Semantic Recognition Based on Modified Fuzzy C-Mean and Maximum Entropy Using Object-to-Object Relations

Abstract

With advances in machine vision systems (e.g., artificial eye, unmanned aerial vehicles, surveillance monitoring) scene semantic recognition (SSR) technology has attracted much attention due to its related applications such as autonomous driving, tourist navigation, intelligent traffic and remote aerial sensing. Although tremendous progress has been made in visual interpretation, several challenges remain (i.e., dynamic backgrounds, occlusion, lack of labeled data, changes in illumination, direction, and size). Therefore, we have proposed a novel SSR framework that intelligently segments the locations of objects, generates a novel Bag of Features, and recognizes scenes via Maximum Entropy. First, denoising and smoothing are applied on scene data. Second, modified Fuzzy C-Means integrates with super-pixels and Random Forest for the segmentation of objects. Third, these segmented objects are used to extract a novel Bag of Features that concatenate different blobs, multiple orientations, Fourier transform and geometrical points over the objects. An Artificial Neural Network recognizes the multiple objects using the different patterns of objects. Finally, labels are estimated via Maximum Entropy model. During experimental evaluation, our proposed system illustrated a remarkable mean accuracy rate of 90.07% over the MSRC dataset and 89.26% over the Caltech 101 for object recognition, and 93.53% over the Pascal-VOC12 dataset for scene recognition, respectively. The proposed system should be applicable to various emerging technologies, such as augmented reality, to represent the real-world environment for military training and engineering design, as well as for entertainment, artificial eyes for visually impaired people and traffic monitoring to avoid congestion or road accidents.

An LSTM-Based Approach for Understanding Human Interactions Using Hybrid Feature Descriptors Over Depth Sensors

“…Some researchers [29,30] also prefer extracting various features and then combining them since hybrid features have yielded better classification results in the past. For example, A. Jalal et al [31] combined four different types of features including blobs, multiple orientations, Fourier transforms, and geometrical points. Similarly, the hybrid features introduced in [32] included energy, sine, distinct body parts movements, and 3D Cartesian views of smoothing gradients.…”

Smartphone Sensor-Based Human Locomotion Surveillance System Using Multilayer Perceptron

“…Shannon Entropy [45] It is the expected amount of information in an instance of the distribution. Other features that include the maximum and the minimum point difference and their ratio in the frequency domain, median, mode, and min and max points of time and frequency domain [51][52][53][54][55][56][57][58][59][60][61][62][63] require a complete mathematical procedure to be followed for their computation, and therefore, we did not mention them in Table 1. The features that are mentioned in Table 1 can be graphically observed in Figure 5.…”

Semantic Recognition of Human-Object Interactions via Gaussian-Based Elliptical Modeling and Pixel-Level Labeling

“…Semantic segmentation has proved to be very effective in multiple domains of image processing and computer vision, such as intelligent transportation, medical imagery, object detection and human-computer interaction [5,6]. Semantic segmentation is the clustering of pixels that belong to the same class and labeling them individually [7]. Therefore, we semantically segment different human body parts and their interaction object.…”