Browsing by Subject "Automatic speech recognition"

Now showing 1 - 4 of 4

Open Access
Adaptive source routing in multistage interconnection networks
(IEEE, 1996-04) Aydoğan, Yücel; Stunkel, C. B.; Aykanat, Cevdet; Abali, B.
We describe the adaptive source routing (ASR) method which is a first attempt to combine adaptive routing and source routing methods. In ASR, the adaptivity of each packet is determined at the source processor. Every packet can be routed in a fully adaptive or partially adaptive or non-adaptive manner, all within the same network at the same time. We evaluate and compare performance of the proposed adaptive source routing networks and oblivious routing networks by simulations. We also describe a route generation algorithm that determines maximally adaptive routes in multistage networks.
Open Access
Combining textual and visual information for semantic labeling of images and videos
(Springer, Berlin, Heidelberg, 2008) Duygulu, Pınar; Baştan, Muhammet; Özkan, Derya; Cord, M.; Cunningham, P.
Semantic labeling of large volumes of image and video archives is difficult, if not impossible, with the traditional methods due to the huge amount of human effort required for manual labeling used in a supervised setting. Recently, semi-supervised techniques which make use of annotated image and video collections are proposed as an alternative to reduce the human effort. In this direction, different techniques, which are mostly adapted from information retrieval literature, are applied to learn the unknown one-to-one associations between visual structures and semantic descriptions. When the links are learned, the range of application areas is wide including better retrieval and automatic annotation of images and videos, labeling of image regions as a way of large-scale object recognition and association of names with faces as a way of large-scale face recognition. In this chapter, after reviewing and discussing a variety of related studies, we present two methods in detail, namely, the so called “translation approach” which translates the visual structures to semantic descriptors using the idea of statistical machine translation techniques, and another approach which finds the densest component of a graph corresponding to the largest group of similar visual structures associated with a semantic description.
Open Access
Multimedia translation for linking visual data to semantics in videos
(Springer, 2011-01) Duygulu, P.; Baştan M.
The semantic gap problem, which can be referred to as the disconnection between low-level multimedia data and high-level semantics, is an important obstacle to build real-world multimedia systems. The recently developed methods that can use large volumes of loosely labeled data to provide solutions for automatic image annotation stand as promising approaches toward solving this problem. In this paper, we are interested in how some of these methods can be applied to semantic gap problems that appear in other application domains beyond image annotation. Specifically, we introduce new problems that appear in videos, such as the linking of keyframes with speech transcript text and the linking of faces with names. In a common framework, we formulate these problems as the problem of finding missing correspondences between visual and semantic data and apply the multimedia translation method. We evaluate the performance of the multimedia translation method on these problems and compare its performance against other auto-annotation and classifier-based methods. The experiments, carried out on over 300 h of news videos from TRECVid 2004 and TRECVid 2006 corpora, show that the multimedia translation method provides a performance that is comparable to the other auto-annotation methods and superior performance compared to other classifier-based methods. © 2009 Springer-Verlag.
Open Access
Translating images to words for recognizing objects in large image and video collections
(Springer, 2006) Duygulu, P.; Baştan M.; Forsyth, D.
We present a new approach to the object recognition problem, motivated by the recent availability of large annotated image and video collections. This approach considers object recognition as the translation of visual elements to words, similar to the translation of text from one language to another. The visual elements represented in feature space are categorized into a finite set of blobs. The correspondences between the blobs and the words are learned, using a method adapted from Statistical Machine Translation. Once learned, these correspondences can be used to predict words corresponding to particular image regions (region naming), to predict words associated with the entire images (autoannotation), or to associate the speech transcript text with the correct video frames (video alignment). We present our results on the Corel data set which consists of annotated images and on the TRECVID 2004 data set which consists of video frames associated with speech transcript text and manual annotations.