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In recent years, interest in scene classification of different indoor-outdoor scene images has increased due to major developments in visual sensor techniques. Scene classification has been demonstrated to be an efficient method for environmental observations but it is a challenging task considering the complexity of multiple objects in scenery images. These images include a combination of different properties and objects i.e., (color, text, and regions) and they are classified on the basis of optimal features. In this paper, an efficient multiclass objects categorization method is proposed for the indoor-outdoor scene classification of scenery images using benchmark datasets. We illustrate two improved methods, fuzzy c-mean and mean shift algorithms, which infer multiple object segmentation in complex images. Multiple object categorization is achieved through multiple kernel learning (MKL), which considers local descriptors and signatures of regions. The relations between multiple objects are then examined by intersection over union algorithm. Finally, scene classification is achieved by using Multi-class Logistic Regression (McLR). Experimental evaluation demonstrated that our scene classification method is superior compared to other conventional methods, especially when dealing with complex images. Our system should be applicable in various domains such as drone targeting, autonomous driving, Global positioning systems, robotics and tourist guide applications.

We present a visual recognition system for fine-grained visual categorization. The system is composed of a human and a machine working together and combines the complementary strengths of computer vision algorithms and (non-expert) human users. The human users provide two heterogeneous forms of information object part clicks and answers to multiple choice questions. The machine intelligently selects the most informative question to pose to the user in order to identify the object class as quickly as possible. By leveraging computer vision and analyzing the user responses, the overall amount of human effort required, measured in seconds, is minimized. Our formalism shows how to incorporate many different types of computer vision algorithms into a human-in-the-loop framework, including standard multiclass methods, part-based methods, and localized multiclass and attribute methods. We explore our ideas by building a field guide for bird identification. The experimental results demonstrate the strength of combining ignorant humans with poor-sighted machines the hybrid system achieves quick and accurate bird identification on a dataset containing 200 bird species.

In recent years, there has been a surge in scientific research using computer vision and robots for precision agriculture. Productivity has increased significantly, and the need for human labor in agriculture has been dramatically reduced owing to technological and mechanical advancements. However, most current apple identification algorithms cannot distinguish between green and red apples on a diverse agricultural field, obscured by tree branches and other apples. A novel and practical target detection approach for robots, using the YOLOV5 framework is presented, in line with the need to recognize apples automatically. Robotic end effectors have been integrated into a Raspberry Pi 4B computer, where the YOLOV5 model has been trained, tested, and deployed. The image was taken with an 8-megapixel camera that uses the camera serial interface (CSI) protocol. To speed up the model creation process, researchers use a graphical processing computer to label and preprocess test images before utilizing them. Using YOLOV5, a computer vision system-assisted framework aids in the design of robotic arms capable of detecting and manipulating objects. The deployed model has performed very well on both red and green apples, with ROC values of 0.98 and 0.9488, respectively. The developed model has achieved a high F1 score with 91.43 for green apples and 89.95 for red apples. The experimental findings showed that robotics are at the forefront of technological advancement because of the rising need for productivity, eliminating monotonous work, and protecting the operator and the environment. The same discerning can be applied to agricultural robots, which have the potential to improve productivity, safety, and profit margins for farmers while reducing their impact on the environment. The system’s potential could be seen in an assortment of fields, including sophisticated object detection, nuanced manipulation, multi-robot collaboration, and field deployment.

We present a multi-modal genre recognition framework that considers the modalities audio, text, and image by features extracted from audio signals, album cover images, and lyrics of music tracks. In contrast to pure learning of features by a neural network as done in the related work, handcrafted features designed for a respective modality are also integrated, allowing for higher interpretability of created models and further theoretical analysis of the impact of individual features on genre prediction. Genre recognition is performed by binary classification of a music track with respect to each genre based on combinations of elementary features. For feature combination a two-level technique is used, which combines aggregation into fixed-length feature vectors with confidence-based fusion of classification results. Extensive experiments have been conducted for three classifier models (Naïve Bayes, Support Vector Machine, and Random Forest) and numerous feature combinations. The results are presented visually, with data reduction for improved perceptibility achieved by multi-objective analysis and restriction to non-dominated data. Feature- and classifier-related hypotheses are formulated based on the data, and their statistical significance is formally analyzed. The statistical analysis shows that the combination of two modalities almost always leads to a significant increase of performance and the combination of three modalities in several cases.

This paper presents the multiple instance classification problem that can be used for drug and molecular activity prediction, text categorization, image annotation, and object recognition. In order to model a more robust representation of outliers, hard margin loss formulations that minimize the number of misclassified instances are proposed. Although the problem is -hard, computational studies show that medium sized problems can be solved to optimality in reasonable time using integer programming and constraint programming formulations. A three-phase heuristic algorithm is proposed for larger problems. Furthermore, different loss functions such as hinge loss, ramp loss, and hard margin loss are empirically compared in the context of multiple instance classification. The proposed heuristic and robust support vector machines with hard margin loss demonstrate superior generalization performance compared to other approaches for multiple instance learning.