A generic framework for semantic video indexing based on visual concepts/contexts detection

Abstract

Providing a semantic access to video data requires the development of concept detectors. However, semantic concepts detection is a hard task due to the large intra-class and the small inter-class variability of content. Moreover, semantic concepts co-occur together in various contexts and their occurrence may vary from one to another. Thus, it is interesting to exploit this knowledge in order to achieve satisfactory performances. In this paper we present a generic semantic video indexing scheme, called SVI_REGIMVid. It is based on three levels of analysis. The first level (level1) focuses on low-level processing such as video shot boundary/ key-frame detection, annotation tools, key-points detection and visual features extraction tools. The second level (level2) aims to build the semantic models for supervised learning of concepts/contexts. The third level (level3) enriches the semantic interpretation of concepts/ contexts by exploiting fuzzy knowledge. The obtained experimental results are promising for a semantic concept/context detection process.

Semantic analysis on social networks: A survey

“…Machine learning‐based approaches use probabilistic methods to obtain appropriate implicit information that will allow the mapping of low‐level visual data to high‐level semantic concepts and entities. Elleuch et al presented a generic semantic video indexing scheme based on three levels of analysis in their work 194 . The first level focuses on low‐level operations such as key frame detection, video shot boundary, annotation tools, key point detection, and visual descriptors of different modalities as Color Histogram, Co‐occurrence Texture, Gabor, and so forth.…”

“…Elleuch et al presented a generic semantic video indexing scheme based on three levels of analysis in their work. 194 The first level focuses on low-level operations such as key frame detection, video shot boundary, annotation tools, key point detection, and visual descriptors of different modalities as Color Histogram, Co-occurrence Texture, Gabor, and so forth. The second level purposes to create semantic models for supervised learning of concepts/contexts.…”