S. H. Srinivasan

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This paper addresses the problem of extracting video objects from MPEG compressed video. The only cues used for object segmentation are the motion vectors which are sparse in MPEG. A method for automatically estimating the number of objects and extracting independently moving video objects using motion vectors is presented here. First, the motion vectors(More)
Features are very important for audio processing. Tasks like speech recognition and instrument identification are based on features. Most low-level features currently used are based on LPC and cepstral analysis. We propose a class of features based on dynamics and harmonicity. In particular, we define the notion of harmonic derivative. The efficacy of the(More)
What are the natural features of hand-written characters and how to arrive at them automatically? We apply independent components analysis on hand-written characters. Independent components analysis extracts the underlying statistically independent signals from a mixure of them. We expect strokes to be the independent components of handwritten characters.(More)
The traditional near-duplicate detection systems developed for digital photo management and copyright protection are not applicable for the de-duplication of large-scale web image corpus. In this paper, we present a fast, accurate and highly scalable image fingerprinting technique suited for near-duplicate detection at the web-scale. The image fingerprint(More)
There have been efforts in the recent years to make home videos look more interesting and pleasing to viewers by mixing it with music. Most of the existing software enables the user to add music of his preference. It assumes that the user has enough knowledge about the aesthetic mixing principles. In our research, we propose a way of adding audio to video(More)
This paper proposes three techniques of feature extraction for person independent action classification in compressed MPEG video. The features used are extracted from motion vectors, obtained by partial decoding of the MPEG video. The feature vectors are fed to Hidden Markov Model (HMM) for classification of actions. Totally seven actions were trained with(More)