Improving content-based image retrieval with compact global and local multi-features

The accuracy of content-based image retrieval (CBIR) systems is significantly affected by the discriminatory power of image features and distance measures. This paper performs an investigation towards finding the best local and global features and distance measures for content-based image retrieval. It provides insights into the trade-offs regarding computational costs, memory utilization and accuracy on several standard datasets which include MIRFLICKR, Corel, Holidays and ZuBuD. First, low-dimensional global and local features are extracted individually to generate a bank of small image features. Second, multilevel descriptor forms are utilized to produce highly discriminative image representations based on multi-features aggregation scheme. The relationship is highlighted between features (local and global) and other retrieval factors such as quantization approaches, visual codebooks, distance measures, vectorization methods, memory and retrieval speed. The resulting composite image representations are compact, i.e., only 32–64 vector dimension and 32–128 codebook size, and preserve high discriminative levels which further boost the retrieval accuracy and performance. The experimental results show that the presented multi-features image representations are efficient and outperform many competitive methods of the state-of-the-art.