Moving Picture Compression using Frame Classification by Luminance Characteristics

Moving Object Detection using Frame Interleaving and Clustering based Compression

Moving Object Detection is one of the key research areas of Image processing. In this regard many researches are underway to provide a novel approach to detect moving object with less space and time complexity. This paper, outlines the novel approach to detect high speed moving object with frame interleaving in frame differencing operation and clustering based compression for frame and backgrou...

Frame-by-frame Phoneme Classification Using Mlp

In this paper, we present some practical experiments for continuous speech frame-by-frame phoneme classification using Multi Layer Perceptron (MLP) neural networks. We used to train and test our software application, the the OASIS Numbers speech database. In our experiments, we tried to classify all the existing 32 phonemes together, from OASIS Numbers database dictionary. We also used differen...

Image classification using compression distance

The normalised compression distance measures the mutual compressibility of two signals. We show that this distance can be used for classification on real images. Furthermore, the same compressor can also operate on derived features with no further modification. We consider derived features consisting of trees indicating the containment and relative area of connected sets within the image. It ha...

Frame-level phoneme classification using inductive inference

Texture Classification Using Sparse Frame-Based Representations

A new method for supervised texture classification, denoted by frame texture classification method (FTCM), is proposed. The method is based on a deterministic texture model in which a small image block, taken from a texture region, is modeled as a sparse linear combination of frame elements. FTCM has two phases. In the design phase a frame is trained for each texture class based on given textur...