3 research outputs found
Deep Multimodal Learning for Audio-Visual Speech Recognition
In this paper, we present methods in deep multimodal learning for fusing
speech and visual modalities for Audio-Visual Automatic Speech Recognition
(AV-ASR). First, we study an approach where uni-modal deep networks are trained
separately and their final hidden layers fused to obtain a joint feature space
in which another deep network is built. While the audio network alone achieves
a phone error rate (PER) of under clean condition on the IBM large
vocabulary audio-visual studio dataset, this fusion model achieves a PER of
demonstrating the tremendous value of the visual channel in phone
classification even in audio with high signal to noise ratio. Second, we
present a new deep network architecture that uses a bilinear softmax layer to
account for class specific correlations between modalities. We show that
combining the posteriors from the bilinear networks with those from the fused
model mentioned above results in a further significant phone error rate
reduction, yielding a final PER of .Comment: ICASSP 201
A Robust Image Watermarking Based on Image Restoration Using SIFT
This paper introduces a novel robust watermarking scheme for digital images, which is robust against common signal processing and geometric distortion attacks. In order to be resistant to geometric distortion attacks, the matched feature points determined by the scale-invariance feature transform (SIFT) are used for image restoration to reduce the synchronization errors caused by geometric distortion attacks. An adaptive embedding scheme is applied in discrete Fourier transform (DFT) domain of each subimage. The watermark detection decision is based on the number of matched bits between the retrieved and original watermark in the embedding image blocks. Experimental results show that the proposed watermarking is robust to common signal processing attacks and geometric distortion attacks, including rotation, scaling, cropping, shearing and some combined attacks
Interactive video retrieval using implicit user feedback.
PhDIn the recent years, the rapid development of digital technologies and the low
cost of recording media have led to a great increase in the availability of
multimedia content worldwide. This availability places the demand for the
development of advanced search engines. Traditionally, manual annotation of
video was one of the usual practices to support retrieval. However, the vast
amounts of multimedia content make such practices very expensive in terms of
human effort. At the same time, the availability of low cost wearable sensors
delivers a plethora of user-machine interaction data. Therefore, there is an
important challenge of exploiting implicit user feedback (such as user navigation
patterns and eye movements) during interactive multimedia retrieval sessions
with a view to improving video search engines. In this thesis, we focus on
automatically annotating video content by exploiting aggregated implicit
feedback of past users expressed as click-through data and gaze movements.
Towards this goal, we have conducted interactive video retrieval experiments, in
order to collect click-through and eye movement data in not strictly controlled
environments. First, we generate semantic relations between the multimedia
items by proposing a graph representation of aggregated past interaction data and
exploit them to generate recommendations, as well as to improve content-based
search. Then, we investigate the role of user gaze movements in interactive video
retrieval and propose a methodology for inferring user interest by employing
support vector machines and gaze movement-based features. Finally, we propose
an automatic video annotation framework, which combines query clustering into
topics by constructing gaze movement-driven random forests and temporally
enhanced dominant sets, as well as video shot classification for predicting the
relevance of viewed items with respect to a topic. The results show that
exploiting heterogeneous implicit feedback from past users is of added value for
future users of interactive video retrieval systems