This paper presents a novel and efficient variable bit rate LPC quantization approach. The proposed MCVQ framework allows a Dynamic Programming based minimum quantization distortion partitioning and quantization process to be performed on input LSP vector tracks in time. Variable duration segments of LSP vector tracks are classified into one of a finite number of language related events. Specif...

Man made indoors environments posses regularities which can be efficiently exploited in automated model acquisition by means of visual sensing. In this context we propose an approach for inferring a topological model of an environment from images or the video stream captured by a mobile robot during exploration. The proposed model consists of a set of locations and neighbourhood relationships b...

Prototype based models offer an intuitive interface to given data sets by means of an inspection of the model prototypes. Supervised classification can be achieved by popular techniques such as learning vector quantization (LVQ) and extensions derived from cost functions such as generalized LVQ (GLVQ) and robust soft LVQ (RSLVQ). These methods, however, are restricted to Euclidean vectors and t...

Two methods to overcome the problems with large vector quantization (VQ) codebooks are lattice VQ (LVQ) and product codes. The approach described in this paper takes advantage of both methods by applying residual VQ with LVQ at all stages. Using LVQ in conjunction with entropy coding is strongly motivated by the fact that entropy constrained but structurally unconstrained VQ design leads to mor...

We propose a new scheme for enlarging generalized learning vector quantization with weighting factors for the several input dimensions which are adapted according to the specific task. This leads to a more powerful classifier with little extra cost as well as the possibility of automatically pruning irrelevant input dimensions. The method is tested on real world satellite image data and compare...

This paper proposes a new method for vector quantization by minimizing the Kullback-Leibler Divergence between the class label distributions over the quantization inputs, which are original vectors, and the output, which is the quantization subsets of the vector set. In this way, the vector quantization output can keep as much information of the class label as possible. An objective function is...

In this paper we propose a three-dimensional vector quantization-based video coding scheme. The algorithm uses a 3D vector quantization pyramidal codebook-based model with adaptive pyramidal codebook for compression. The pyramidal codebook-based model helps in getting high compression in case of modest motion. The adaptive vector quantization algorithm is used to train the codebook for optimal ...

We present a category learning vector quantization (cLVQ) approach for incremental and life-long learning of multiple visual categories where we focus on approaching the stability-plasticity dilemma. To achieve the life-long learning ability an incremental learning vector quantization approach is combined with a category-specific feature selection method in a novel way to allow several metrical...

We extend a recent variant of the prototype-based classifier learning vector quantization to a scheme which locally adapts relevance terms during learning. We derive explicit dimensionality-independent large-margin generalization bounds for this classifier and show that the method can be seen as margin maximizer.