Efficient Object Segmentation and Recognition Using Multi-Layer Perceptron Networks
Source abstract
Abstract
Object segmentation and recognition is an imperative area of computer vision and machine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their features. The proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks (ANNs). The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label them based on their characteristics. Then, two distinct kinds of features are obtained from the segmented images to help identify the objects of interest. An Artificial Neural Network (ANN) is then used to recognize the objects based on their features. Experiments were carried out with three standard datasets, MSRC, MS COCO, and Caltech 101 which are extensively used in object recognition research, to measure the productivity of the suggested approach. The findings from the experiment support the suggested system’s validity, as it achieved class recognition accuracies of 89%, 83%, and 90.30% on the MSRC, MS COCO, and Caltech 101 datasets, respectively.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Advanced Image and Video Retrieval Techniques · Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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