Hashing for Multimedia Similarity Modeling and Large-Scale Retrieval

In recent years, the amount of multimedia data such as images, texts, and videos have been growing rapidly on the Internet. Motivated by such trends, this thesis is dedicated to exploiting hashing-based solutions to reveal multimedia data correlations and support intra-media and inter-media similarity search among huge volumes of multimedia data. We start by investigating a hashing-based solution for audio-visual similarity modeling and apply it to the audio-visual sound source localization problem. We show that synchronized signals in audio and visual modalities demonstrate similar temporal changing patterns in certain feature spaces. We propose to use a permutation-based random hashing technique to capture the temporal order dynamics of audio and visual features by hashing them along the temporal axis into a common Hamming space. In this way, the audio-visual correlation problem is transformed into a similarity search problem in the Hamming space. Our hashing-based audio-visual similarity modeling has shown superior performances in the localization and segmentation of sounding objects in videos. The success of the permutation-based hashing method motivates us to generalize and formally define the supervised ranking-based hashing problem, and study its application to large-scale image retrieval. Specifically, we propose an effective supervised learning procedure to learn optimized ranking-based hash functions that can be used for large-scale similarity search. Compared with the randomized version, the optimized ranking-based hash codes are much more compact and discriminative. Moreover, it can be easily extended to kernel space to discover more complex ranking structures that cannot be revealed in linear subspaces. Experiments on large image datasets demonstrate the effectiveness of the proposed method for image retrieval. We further studied the ranking-based hashing method for the cross-media similarity search problem. Specifically, we propose two optimization methods to jointly learn two groups of linear subspaces, one for each media type, so that features\u27 ranking orders in different linear subspaces maximally preserve the cross-media similarities. Additionally, we develop this ranking-based hashing method in the cross-media context into a flexible hashing framework with a more general solution. We have demonstrated through extensive experiments on several real-world datasets that the proposed cross-media hashing method can achieve superior cross-media retrieval performances against several state-of-the-art algorithms. Lastly, to make better use of the supervisory label information, as well as to further improve the efficiency and accuracy of supervised hashing, we propose a novel multimedia discrete hashing framework that optimizes an instance-wise loss objective, as compared to the pairwise losses, using an efficient discrete optimization method. In addition, the proposed method decouples the binary codes learning and hash function learning into two separate stages, thus making the proposed method equally applicable for both single-media and cross-media search. Extensive experiments on both single-media and cross-media retrieval tasks demonstrate the effectiveness of the proposed method

Artificial Intelligence Tools for Facial Expression Analysis.

Inner emotions show visibly upon the human face and are understood as a basic guide to an individual’s inner world. It is, therefore, possible to determine a person’s attitudes and the effects of others’ behaviour on their deeper feelings through examining facial expressions. In real world applications, machines that interact with people need strong facial expression recognition. This recognition is seen to hold advantages for varied applications in affective computing, advanced human-computer interaction, security, stress and depression analysis, robotic systems, and machine learning. This thesis starts by proposing a benchmark of dynamic versus static methods for facial Action Unit (AU) detection. AU activation is a set of local individual facial muscle parts that occur in unison constituting a natural facial expression event. Detecting AUs automatically can provide explicit benefits since it considers both static and dynamic facial features. For this research, AU occurrence activation detection was conducted by extracting features (static and dynamic) of both nominal hand-crafted and deep learning representation from each static image of a video. This confirmed the superior ability of a pretrained model that leaps in performance. Next, temporal modelling was investigated to detect the underlying temporal variation phases using supervised and unsupervised methods from dynamic sequences. During these processes, the importance of stacking dynamic on top of static was discovered in encoding deep features for learning temporal information when combining the spatial and temporal schemes simultaneously. Also, this study found that fusing both temporal and temporal features will give more long term temporal pattern information. Moreover, we hypothesised that using an unsupervised method would enable the leaching of invariant information from dynamic textures. Recently, fresh cutting-edge developments have been created by approaches based on Generative Adversarial Networks (GANs). In the second section of this thesis, we propose a model based on the adoption of an unsupervised DCGAN for the facial features’ extraction and classification to achieve the following: the creation of facial expression images under different arbitrary poses (frontal, multi-view, and in the wild), and the recognition of emotion categories and AUs, in an attempt to resolve the problem of recognising the static seven classes of emotion in the wild. Thorough experimentation with the proposed cross-database performance demonstrates that this approach can improve the generalization results. Additionally, we showed that the features learnt by the DCGAN process are poorly suited to encoding facial expressions when observed under multiple views, or when trained from a limited number of positive examples. Finally, this research focuses on disentangling identity from expression for facial expression recognition. A novel technique was implemented for emotion recognition from a single monocular image. A large-scale dataset (Face vid) was created from facial image videos which were rich in variations and distribution of facial dynamics, appearance, identities, expressions, and 3D poses. This dataset was used to train a DCNN (ResNet) to regress the expression parameters from a 3D Morphable Model jointly with a back-end classifier

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Local ordinal contrast pattern histograms for spatiotemporal, lip-based speaker authentication

Lip region deformation during speech contains biometric information and is termed visual speech. This biometric information can be interpreted as being genetic or behavioral depending on whether static or dynamic features are extracted. In this paper, we use a texture descriptor called local ordinal contrast pattern (LOCP) with a dynamic texture representation called three orthogonal planes to represent both the appearance and dynamics features observed in visual speech. This feature representation, when used in standard speaker verification engines, is shown to improve the performance of the lip-biometric trait compared to the state-of-the-art. The best baseline state-of-the-art performance was a half total error rate (HTER) of 13.35% for the XM2VTS database. We obtained HTER of less than 1%. The resilience of the LOCP texture descriptor to random image noise is also investigated. Finally, the effect of the amount of video information on speaker verification performance suggests that with the proposed approach, speaker identity can be verified with a much shorter biometric trait record than the length normally required for voice-based biometrics. In summary, the performance obtained is remarkable and suggests that there is enough discriminative information in the mouth-region to enable its use as a primary biometric trait

Local ordinal contrast pattern histograms for spatiotemporal, lip-based speaker authentication

Lip region deformation during speech contains biometric information and is termed visual speech. This biometric information can be interpreted as being genetic or behavioral depending on whether static or dynamic features are extracted. In this paper, we use a texture descriptor called local ordinal contrast pattern (LOCP) with a dynamic texture representation called three orthogonal planes to represent both the appearance and dynamics features observed in visual speech. This feature representation, when used in standard speaker verification engines, is shown to improve the performance of the lip-biometric trait compared to the state-of-the-art. The best baseline state-of-the-art performance was a half total error rate (HTER) of 13.35% for the XM2VTS database. We obtained HTER of less than 1%. The resilience of the LOCP texture descriptor to random image noise is also investigated. Finally, the effect of the amount of video information on speaker verification performance suggests that with the proposed approach, speaker identity can be verified with a much shorter biometric trait record than the length normally required for voice-based biometrics. In summary, the performance obtained is remarkable and suggests that there is enough discriminative information in the mouth-region to enable its use as a primary biometric trait

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p