Recently much attention has been paid to Image ContentBased Retrieval Systems (CBRS). One important goal in CBRS is to extract local low level image features such as color, texture and shape, to allow queries based on these features. A large CBRS containing tens of thousands of images requires an automatic feature-extraction method since human aided segmentation is impractical. We address this ...

We investigate how to represent a natural image in order to be able to recognize the visual concepts within it. The core of the proposed method consists in a new approach to aggregate local features, based on a non-parametric estimation of the Fisher vector, that result from the derivation of the gradient of the loglikelihood. For this, we need to use low level local descriptors that are learne...

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...

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...

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 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...

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...