This work presents an approach on high-level semantic feature detection in video sequences. Keyframes are selected to represent the visual content of the shots. Then, low-level feature extraction is performed on the keyframes 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 cluste...

Opinion mining deals with an analysis of user reviews for extracting their opinions, sentiments and demands in a specific area, which can play an important role in making major decisions in such area. In general, opinion mining extracts user reviews at three levels of document, sentence and feature. Opinion mining at the feature level is taken into consideration more than the other two levels d...

A hierarchical framework to perform automatic categorization and reorientation of consumer images based on their content is presented. Sometimes the consumer rotates the camera while taking the photographs but the user has to later correct the orientation manually. The present system works in such cases; it first categorizes consumer images in a rotation invariant fashion and then detects their...

A feature model captures various possible configurations of products within a product family. When configuring a product, several features are selected and composed. Selecting features at the program level has a general limitation of not being able to relate the resulting configuration to its requirements. As a result, it is difficult to decide whether a given configuration of features is optim...

Several methods have recently been proposed to analyze speech and automatically infer the personality of the speaker. These methods often rely on prosodic and other hand crafted speech processing features extracted with off-the-shelf toolboxes. To achieve high accuracy, numerous features are typically extracted using complex and highly parameterized algorithms. In this paper, a new method based...

In this paper, we described the video high-level feature extraction systems developed at France Telecom Orange Labs (Beijing). In our systems, four categories of lowlevel visual features, namely color, edge, texture and SIFT local descriptors, were extracted. Two approaches to fusing the representative capabilities of these visual features were investigated for different runs. Under the setting...

The libxtract library consists of a collection of over forty functions that can be used for the extraction of low level audio features. In this paper I will describe the development and usage of the library as well as the rationale for its design. Its use in the composition and performance of music involving live electronics also will be discussed. A number of use case scenarios will be present...

In this study we present our system for INTERSPEECH 2014 Computational Paralinguistics Challenge (ComParE 2014), Physical Load Sub-challenge (PLS). Our contribution is twofold. First, we propose using Low Level Descriptor (LLD) information as hints, so as to partition the feature space into meaningful subsets called views. We also show the virtue of commonly employed feature projections, such a...

In the high-level operations of computer vision it is taken for granted that image features have been reliably detected. This paper addresses the problem of feature extraction by scale-space methods. This paper is based on two key ideas: to investigate the stochastic properties of scale-space representations and to investigate the interplay between discrete and continuous images. These investig...

Representational commitments i.e., the choice of features or attributes that are used to describe the data presented to a learner, and the level of detail at which they describe the data, can have a major impact on the difficulty of learning, and the accuracy, complexity, and comprehensibility of the learned predictive model. The representation has to be rich enough to capture the distinctions ...