Browsing by Subject "Codebooks"
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Item Open Access Design of trellis waveform coders with near-optimum structure(IET, 1992) Kuruoglu, E.E.; Ayanoglu, E.In this Letter the combinatorial optimisation algorithm known as simulated annealing is used for the optimisation of the trellis structure of the next-state map of the decoder finite-state machine in trellis waveform coding. The generalised Lloyd algorithm which finds the optimum codebook is incorporated into simulated annealing so that near-optimum coding systems are designed. Comparison of simulation results with previous work in the literature shows that this method yields better coding systems than those published in the literature.Item Open Access Nearest-neighbor based metric functions for indoor scene recognition(Academic Press, 2011) Cakir, F.; Güdükbay, Uğur; Ulusoy, ÖzgürIndoor scene recognition is a challenging problem in the classical scene recognition domain due to the severe intra-class variations and inter-class similarities of man-made indoor structures. State-of-the-art scene recognition techniques such as capturing holistic representations of an image demonstrate low performance on indoor scenes. Other methods that introduce intermediate steps such as identifying objects and associating them with scenes have the handicap of successfully localizing and recognizing the objects in a highly cluttered and sophisticated environment. We propose a classification method that can handle such difficulties of the problem domain by employing a metric function based on the Nearest-Neighbor classification procedure using the bag-of-visual words scheme, the so-called codebooks. Considering the codebook construction as a Voronoi tessellation of the feature space, we have observed that, given an image, a learned weighted distance of the extracted feature vectors to the center of the Voronoi cells gives a strong indication of the image's category. Our method outperforms state-of-the-art approaches on an indoor scene recognition benchmark and achieves competitive results on a general scene dataset, using a single type of descriptor. © 2011 Elsevier Inc. All rights reserved.Item Open Access Scene classification using bag-of-regions representations(IEEE, 2007-06) Gökalp, Demir; Aksoy, SelimThis paper describes our work on classification of outdoor scenes. First, images are partitioned into regions using one-class classification and patch-based clustering algorithms where one-class classifiers model the regions with relatively uniform color and texture properties, and clustering of patches aims to detect structures in the remaining regions. Next, the resulting regions are clustered to obtain a codebook of region types, and two models are constructed for scene representation: a "bag of individual regions" representation where each region is regarded separately, and a "bag of region pairs" representation where regions with particular spatial relationships are considered, together. Given these representations, scene classification is done using Bayesian classifiers. We also propose a novel region selection algorithm that identifies region types that are frequently found in a particular class of scenes but rarely exist in other classes, and also consistently occur together in the same class of scenes. Experiments on the LabelMe data set showed that the proposed models significantly out-perform a baseline global feature-based approach. © 2007 IEEE.