Multimedia Indexing and Retrieval: Optimized Combination of Low-level and High-level Features

Nowadays, the number of theoretical studies that deal with classification and machine learning from a general point of view, without focusing on a particular application, remains very low. Although they address real problems such as the combination of visual (low-level) and semantic (high-level) descriptors, these studies do not provide a general approach that gives satisfying results in all cases. However, the implementation of a general approach will not go without asking the following questions: (i) How to model the combination of the information produced by both low-level and high-level features? (ii) How to assess the robustness of a given method on different applications ? We try in this study to address these questions that remain open-ended and challenging. We proposes a new semantic video search engine called "SIRI". It combines 3 subsystems based on the optimized combination of low-level and high-level features to improve the accuracy of data retrieval. Performance analysis shows that our SIRI system can raise the average accuracy metrics from 92% to 100% for the Beach category, and from 91% to 100% for the Mountain category over the ISE system using Corel dataset. Moreover, SIRI improves the average accuracy by 99% compared to 95% for the ISE. In fact, our system improves indexing for different concepts compared to both VINAS and VISEN systems. For example, the value of the traffic concept rises from 0.3 to 0.5 with SIRI. It is positively reflected on the search result using the TRECVID 2015 dataset, and increases the average accuracy by 98.41% compared to 85% for VINAS and 88% for VISEN.