Image Annotation With YCbCr Color Features Based on Multiple Deep CNN- GLP

Abstract

Digital image collections are becoming increasingly popular due to their ease of use. Still, the need for adequate indexing information makes it difficult for users to find the specific images they need. With the vast number of digital images generated daily, these databases have become enormous, making accurate image retrieval challenging. One of the most challenging tasks in computer vision and multimedia research is image annotation, where keywords are assigned to an image. Unlike humans, computers can measure colors, textures, and shapes of images but fail to interpret them semantically, known as the semantic gap. This makes image annotation complex. For semantic-level concepts generation the raw image pixels provide not enough Unmistakable information. Which mean for of “words” or “sentences” there is no clear definition with the semantics of an image unlike text annotation. Therefore, this study aims to bridge the semantic gap between low-level computer features and human interpretation of images. The proposed enhanced automatic image annotation system maps multiple labels or into single image, providing an in-depth understanding of the visual content’s meaning. This is achieved by combining Convolutional Neural Networks-based multiple features (Y is the green component of the color, Cb and Cr is the blue component and red component called YCbCr color space and Gaussian–Laplacian Pyramid) and neighbors to recall and balance precision. The image annotation (IA) scheme uses a Global Vectors for Word Representation (GloVe) model with CNN-Gaussian–Laplacian Pyramid and learning representation to predict image annotation (IA) accurately. The proposed image annotation (IA) system was execution on three public datasets and showed excellent flexibility of annotation, improved accuracy, and reduced computational costs compared to existing state-of-the-art methods. The image annotation (IA) framework can provide immense benefits in accurately selecting and extracting image features, minimizing computational complexity and facilitating annotation.

Keywords

• Features extraction
• YCbCr color
• digital learning
• Gaussian–Laplacian pyramid
• image annotation
• technological development