This paper investigates the recognition of group actions in meetings. A framework is employed in which group actions result from the interactions of the individual participants. The group actions are modeled using different HMM-based approaches, where the observations are provided by a set of audiovisual features monitoring the actions of individuals. Experiments demonstrate the importance of taking interactions into account in modeling the group actions. It is also shown that the visual modality contains useful information, even for predominantly audio-based events, motivating a multimodal approach to meeting analysis.

We present an image retrieval system driven by free-hand sketched queries depicting shape. We introduce Gradient Field HoG (GF-HOG) as a depiction invariant image descriptor, encapsulating local spatial structure in the sketch and facilitating efficient codebook based retrieval. We show improved retrieval accuracy over 3 leading descriptors (Self Similarity, SIFT, HoG) across two datasets (Flickr160, ETHZ extended objects), and explain how GF-HOG can be combined with RANSAC to localize sketched objects within relevant images. We also demonstrate a prototype sketch driven photo montage application based on our system.

Abstract-The probability hypothesis density (PHD) filter based on sequential Monte Carlo (SMC) approximation (also known as SMC-PHD filter) has proven to be a promising algorithm for multi-speaker tracking. However, it has a heavy computational cost as surviving, spawned and born particles need to be distributed in each frame to model the state of the speakers and to estimate jointly the variable number of speakers with their states. In particular, the computational cost is mostly caused by the born particles as they need to be propagated over the entire image in every frame to detect the new speaker presence in the view of the visual tracker. In this paper, we propose to use audio data to improve the visual SMC-PHD (V-SMC-PHD) filter by using the direction of arrival (DOA) angles of the audio sources to determine when to propagate the born particles and re-allocate the surviving and spawned particles. The tracking accuracy of the AV-SMC-PHD algorithm is further improved by using a modified mean-shift algorithm to search and climb density gradients iteratively to find the peak of the probability distribution, and the extra computational complexity introduced by mean-shift is controlled with a sparse sampling technique. These improved algorithms, named as AVMS-SMC-PHD and sparse-AVMS-SMC-PHD respectively, are compared systematically with AV-SMC-PHD and V-SMC-PHD based on the AV16.3, AMI and CLEAR datasets.

In this paper, we generalise multiple kernel Fisher discriminant analysis (MK-FDA) such that the kernel weights can be regularised with an p norm for any p ≥ 1, in contrast to existing MK-FDA that uses either l1 or l2 norm. We present formulations for both binary and multiclass cases and solve the associated optimisation problems efficiently with semi-infinite programming. We show on three object and image categorisation benchmarks that by learning the intrinsic sparsity of a given set of base kernels using a validation set, the proposed p MK-FDA outperforms its fixed-norm counterparts, and is capable of producing stateof-the-art performance. Moreover, we show that our p MK-FDA outperforms the p multiple kernel support vector machine ( p MK-SVM) which has been recently proposed. Based on this observation and our experience with single kernel FDA and SVM, we argue that the almost century-old FDA is still a strong competitor of the popular SVM.

In existing audio-visual blind source separation (AV-BSS) algorithms, the AV coherence is usually established through statistical modelling, using e.g., Gaussian mixture models (GMMs). These methods often operate in a low-dimensional feature space, rendering an effective global representation of the data. The local information, which is important in capturing the temporal structure of the data, however, has not been explicitly exploited. In this paper, we propose a new method for capturing such local information, based on audio-visual dictionary learning (AVDL). We address several challenges associated with AVDL, including cross-modality differences in size, dimension and sampling rate, as well as the issues of scalability and computational complexity. Following a commonly employed bootstrap coding-learning process, we have developed a new AVDL algorithm which features, a bimodality balanced and scalable matching criterion, a size and dimension adaptive dictionary, a fast search index for efficient coding, and cross-modality diverse sparsity. We also show how the proposed AVDL can be incorporated into a BSS algorithm. As an example, we consider binaural mixtures, mimicking aspects of human binaural hearing, and derive a new noise-robust AV-BSS algorithm by combining the proposed AVDL algorithm with Mandel's BSS method, which is a state-of-the-art audio-domain method using time-frequency masking. We have systematically evaluated the proposed AVDL and AV-BSS algorithms, and show their advantages over the corresponding baseline methods, using both synthetic data and visual speech data from the multimodal LILiR Twotalk corpus.Index Terms-Audio-visual coherence, blind source separation, convolutive mixtures, dictionary learning, noisy mixtures. 1053-587X © 2013 IEEE