− Audio-Visual Automatic Speech Recognition systems use visual information to enhance ASR systems in clean and noisy environments. This paper compares of a number of different visual feature extraction methods. When performing visual speech recognition the visual feature vector requires a base level of detail for optimum recognition. Geometric feature extraction provides lower recognition than ...

Classification on high-dimensional data with thousands to tens of thousands of dimensions is a challenging task due to the high dimensionality and the quality of the feature set. The problem can be addressed by using feature selection to choose only informative features or feature construction to create new high-level features. Genetic programming (GP) using a tree-based representation can be u...

This paper presents a framework for the detection of semantic features in video sequences. Low-level feature extraction is performed on the keyframes of the shots and a “feature vector” including color and texture features is formed. A region “thesaurus” that contains all the high-level features is constructed using a subtractive clustering method.Then, a “model vector” that contains the distan...

This paper provides a new approach to recognize facial expressions. In this paper, facial expression recognition is based on appearance based features or we can say that low level features. We used two different approaches to categories the expression into seven different classes. These classifications based on Scale Invariant Feature Transform (SIFT) and Local Gabor Binary Filter (LGBP). First...

The IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes our participation to the TRECVID 2008 High Level Features detection task. We evaluated several fusion strategies and especially rank fusion. Results show that including as many low-level and intermediate features as possible is the best strategy, that SIFT features are very importan...

Semantic retrieval from video databases is becoming a very important research topic in the area of multimedia. This kind of tasks require the development of video data representation models which include the relationships between low-level visual cues and the semantic concepts inferred from them. This paper presents a work based on semiotic studies that includes the extraction of simple visual ...

Introduction Building robust low-level image representations, beyond edge primitives, is a long-standing goal in vision. In its most basic form, an image is a matrix of intensities. How we should progress from this matrix to stable mid-level representations, useful for high-level vision tasks, remains unclear. Popular feature representations such as SIFT or HOG spatially pool edge information t...