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...

The hypothesis veri cation stage of the traditional image processing approach, consisting of low, medium, and high level processing, will su er if the set of low level features extracted are of poor quality. We investigate the optimisation of the feature extraction chain by using Genetic Algorithms. The tness function is a performance measure which re ects the quality of an extracted set of fea...

Much of the world’s supply of data is in the form of time series. In the last decade, there has been an explosion of interest in mining time series data. A number of new algorithms have been introduced to classify, cluster, segment, index, discover rules, and detect anomalies/novelties in time series. While these many different techniques used to solve these problems use a multitude of differen...

This paper describes our experiments for the high level feature extraction task in TRECVid 2007. We submitted the following five runs: • A jr1 1: Baseline run using early fusion of all input features. • A jr1 2: Classic early feature fusion and concept correlation. • A jr1 3: Classic late feature fusion. • A jr1 4: Late feature fusion and concept correlation. • A jr1 5: Early fusion of heuristi...

In this paper we propose a novel feature extraction scheme for texture classi cation, in which the texture features are extracted by a two-level hybrid scheme by integrating two statistical techniques of texture analysis. In the rst step, the low level features are extracted by the Gabor lters, and they are encoded with the feature map indices using the Kohonen's SOFM algorithm. In the next ste...

In this work an adaptive method for accurate and robust grouping of local features belonging to planes of interior scenes and object planar surfaces is presented. For arbitrary set of images acquired from different views, the method organizes a huge number of local SIFT features to fill the gap between low-level vision (front end) and high level vision, i.e. domain specific reasoning about geom...