A comparison study on image content based retrieval systems

In recent years, multimedia searching has become an important research field. Multimedia files are one of the most important materials on the internet. Unfortunately, even for the state-of-the-art methods and applications based on the access to multimedia on the internet, it is hard to find the required multimedia. The main purpose of this study is to investigate the performance of well-known image content-based retrieval techniques, i.e., Fuzzy Color and Texture Histogram (FCTH), Edge Histogram Descriptor (EHD), Scalable Color Descriptor (SCD), Color Layout Descriptor (CLD), Color and Edge Directivity Descriptor (CEDD), and Speed-Up Robust Feature (SURF) combined with Fast Library Approximate Nearest Neighbor (FLANN). The objective of using these techniques is to find the query's most relevant files and list them at the top of the retrieval list. Several experiments have been conducted and it has been observed that FCTH and SCD outperform other studied techniques. On the other hand, for the SURF combined with FLANN approach, the results of most of the queries were below user expectations. In addition, extracting the feature vectors using this method requires massive amount of memory. Overall, none of the studied CBIR descriptors can be used individually to build a full image retrieval system. In our opinion, multiple descriptors can be used to achieve a more robust system and accurate results.