Research on Content-Based Image Retrieval Algorithms

This research aims to investigate content-based image retrieval algorithms, with special emphasis on the color histogram algorithm. The study will delve into the fundamental principles and practical applications of color histogram algorithms, examining both their advantages and limitations in image retrieval systems. We will explore key implementation aspects such as color space selection (RGB, HSV, or Lab), histogram bin configuration, and distance metrics (Euclidean, Manhattan, or Chi-square) for similarity measurement. Additionally, the research will analyze other relevant image feature extraction algorithms including texture descriptors (such as LBP or Gabor filters) and shape-based features to conduct performance comparisons with color histogram approaches. Through this comprehensive investigation, we intend to provide deeper insights and understanding that will contribute to the advancement and practical application of content-based image retrieval algorithms. The study will include code-level discussions on feature vector normalization techniques and efficient indexing methods for large-scale image databases.