Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper WeBCT9.38
Efficient Semantic Indexing for Image Retrieval
Pulla, Chandrika, International Inst. of Information Tech. Hyderabad
Karthik, Suman, International Inst. of Information Tech. Hyderabad
Jawahar, C. V., IIIT
Semantic analysis of a document collection can be viewed as an unsupervised clustering of the constituent words and documents
around hidden or latent concepts. This has shown to improve the performance of visual bag of words in image retrieval.
However, the enhancement in performance depends heavily on the right choice of number of semantic concepts.
Most of the semantic indexing schemes are also computationally costly. In this paper, we employ a bipartite graph model
(BGM) for image retrieval. BGM is a scalable data structure that aids semantic indexing in an efficient manner. It can also
be incrementally updated. BGM uses \textbf{tf-idf} values for building a semantic bipartite graph. We also introduce a
graph partitioning algorithm that works on the BGM to retrieve semantically relevant images from a database. We demonstrate
the properties as well as performance of our semantic indexing scheme through a series of experiments. We also
compare our methods with incremental pLSA.
13:30-16:30, Paper WeBCT9.39
Improving and Aligning Speech with Presentation Slides
Swaminathan, Ranjini, Univ. of Arizona
Thompson, Michael E., Univ. of Arizona
Fong, Sandiway, Univ. of Arizona
Efrat, Alon, Univ. of Arizona
Amir, Arnon
Barnard, Kobus, Univ. of Arizona
We present a novel method to correct automatically generated speech transcripts of talks and lecture videos using text
from accompanying presentation slides. The approach finesses the challenges of dealing with technical terms which are
often outside the vocabulary of speech recognizers. Further, we align the transcript to the slide word sequence so that we
can improve the organization of closed captioning for hearing impaired users, and improve automatic highlighting or magnification
for visually impaired users. For each speech segment associated with a slide, we construct a sequential Hidden
Markov Model for the observed phonemes that follows slide word order, interspersed with text not on the slide. Incongruence
between slide words and mistaken transcript words is accounted for using phoneme confusion probabilities. Hence,
transcript words different from aligned high probability slide words can be corrected. Experiments on six talks show improvement
in transcript accuracy and alignment with slide words.
13:30-16:30, Paper WeBCT9.40
The ImageCLEF Medical Retrieval Task at ICPR 2010 - Information Fusion
Kalpathy-Cramer, Jayashree, Oregon Health & Science Univ.
Müller, Henning, Univ. of Applied Sciences
An increasing number of clinicians, researchers, educators and patients routinely search for medical information on the
Internet as well as in image archives. However, image retrieval is far less understood and developed than text-based search.
The ImageCLEF medical image retrieval task is an international benchmark that enables researchers to assess and compare
techniques for medical image retrieval using standard test collections. Although text retrieval is mature and well researched,
it is limited by the quality and availability of the annotations associated with the images. Advances in computer vision
have led to methods for using the image itself as search entity. However, the success of purely content-based techniques
has been limited and these systems have not had much clinical success. On the other hand a combination of text- and content-based
retrieval can achieve improved retrieval performance if combined effectively. Combining visual and textual
runs is not trivial based on experience in ImageCLEF. The goal of the fusion challenge at ICPR is to encourage participants
to combine visual and textual results to improve search performance. Participants were provided textual and visual runs,
as well as the results of the manual judgments from ImageCLEFmed 2008 as training data. The goal was to combine
textual and visual runs from 2009. In this paper, we present the results from this ICPR contest.
13:30-16:30, Paper WeBCT9.41
Unified Approach to Detection and Identification of Commercial Films by Temporal Occurrence Pattern
Putpuek, Narongsak, Chulalongkorn Univ.
- 237 -