SAVASA project @ TRECVID 2012: interactive surveillance event detection

In this paper we describe our participation in the interactive surveillance event detection task at TRECVid 2012. The system we developed was comprised of individual classifiers brought together behind a simple video search interface that enabled users to select relevant segments based on down~sampled animated gifs. Two types of user -- `experts' and `end users' -- performed the evaluations. Due to time constraints we focussed on three events -- ObjectPut, PersonRuns and Pointing -- and two of the five available cameras (1 and 3). Results from the interactive runs as well as discussion of the performance of the underlying retrospective classifiers are presented

Later life, inequality and sociological theory

A central concern of many theorists of later life has been to elucidate the processes which shape the marginalisation and relative disadvantage of older people in contemporary society. This concern parallels a current argument within sociological theorising: that life course stage and generational location constitute increasingly important dimensions of social difference and inequality. It is an argument of the paper that many current approaches operate with metaphors of society which ultimately locate those in later life at the margins by virtue of the theoretical terms being used. Too much has been claimed for life course-based divisions and too little has been claimed in respect of life course-related processes. The paper develops an alternative, moral economy, perspective with the aim of furthering analysis of the social organisation of life course-related rights, claims and obligations and their relationship to lifetime inequalities across the population. Such an approach offers a resourceful framework both for interrogating the diverse circumstances and experiences of those in later life, and for conceptualising social inequality and its reproduction

Action recognition based on sparse motion trajectories

We present a method that extracts effective features in videos for human action recognition. The proposed method analyses the 3D volumes along the sparse motion trajectories of a set of interest points from the video scene. To represent human actions, we generate a Bag-of-Features (BoF) model based on extracted features, and finally a support vector machine is used to classify human activities. Evaluation shows that the proposed features are discriminative and computationally efficient. Our method achieves state-of-the-art performance with the standard human action recognition benchmarks, namely KTH and Weizmann datasets

Culture modulates implicit ownership-induced self-bias in memory

The relation of incoming stimuli to the self implicitly determines the allocation of cognitive resources. Cultural variations in the self-concept shape cognition, but the extent is unclear because the majority of studies sample only Western participants. We report cultural differences (Asian versus Western) in ownership-induced self-bias in recognition memory for objects. In two experiments, participants allocated a series of images depicting household objects to self-owned or other-owned virtual baskets based on colour cues before completing a surprise recognition memory test for the objects. The ‘other’ was either a stranger or a close other. In both experiments, Western participants showed greater recognition memory accuracy for self-owned compared with other-owned objects, consistent with an independent self-construal. In Experiment 1, which required minimal attention to the owned objects, Asian participants showed no such ownership-related bias in recognition accuracy. In Experiment 2, which required attention to owned objects to move them along the screen, Asian participants again showed no overall memory advantage for self-owned items and actually exhibited higher recognition accuracy for mother-owned than self-owned objects, reversing the pattern observed for Westerners. This is consistent with an interdependent self-construal which is sensitive to the particular relationship between the self and other. Overall, our results suggest that the self acts as an organising principle for allocating cognitive resources, but that the way it is constructed depends upon cultural experience. Additionally, the manifestation of these cultural differences in self-representation depends on the allocation of attentional resources to self- and other-associated stimuli

A review of multi-instance learning assumptions

Multi-instance (MI) learning is a variant of inductive machine learning, where each learning example contains a bag of instances instead of a single feature vector. The term commonly refers to the supervised setting, where each bag is associated with a label. This type of representation is a natural fit for a number of real-world learning scenarios, including drug activity prediction and image classification, hence many MI learning algorithms have been proposed. Any MI learning method must relate instances to bag-level class labels, but many types of relationships between instances and class labels are possible. Although all early work in MI learning assumes a specific MI concept class known to be appropriate for a drug activity prediction domain; this ‘standard MI assumption’ is not guaranteed to hold in other domains. Much of the recent work in MI learning has concentrated on a relaxed view of the MI problem, where the standard MI assumption is dropped, and alternative assumptions are considered instead. However, often it is not clearly stated what particular assumption is used and how it relates to other assumptions that have been proposed. In this paper, we aim to clarify the use of alternative MI assumptions by reviewing the work done in this area

Constructive Interpretation in Design Thinking

This thesis explores the role of interpretation in design activity through the development of a computational model of constructive interpretation. It asks the question: how does the construction of interpretations from expectations within a situation affect design activity? This work hypothesises that designers construct their world from their expectations through interpretation. In interpreting their own work designers are able to make unexpected discoveries and explore the implicit knowledge held within their expectations of the world. These expectations are grounded in experience. A conceptual model for constructive interpretation is described. Knowledge held by designers is represented in a perceptual symbol system, in which knowledge organised in a hierarchy. Within this hierarchy, higher layers represent an increased level of abstraction. Knowledge is learnt through experience in an environment. The topmost layer in this hierarchy is the situation.Interpretation occurs through pull from the expectations. Expectations in a layer are changed by the layer above. The construction of expectations utilises knowledge about the world that the designer gains through experience. A computational framework for this conceptual model is described: (i) based upon conceptual spaces, where expectations within the situation perturb each other; and (ii) based upon a hierarchy of unsupervised learning networks, where prototypes represent convergence zones within conceptual space. Constructive interpretation is implemented in a number of demonstrations utilising modified self-organising maps linked together to represent layers in the conceptual model. Demonstrations show: (i) how situations are changed through construction from implicit expectations; (ii) how situations co-ordinate concepts through expectations that are grounded in experience; (iii) how construction from expectations produces stability in a chang ing environment; and (iv) how useful rather than accurate in! terpreta tions can be produced by constructing from expectations. A model of constructive interpretation in design is developed in which a system iterates through generation of designs from expectations and constructive interpretation. In one experiment an agent has experience with a number of floor plans. It uses its experience to draw in a design medium and interpret its own work. Through constructive interpretation from implicit expectations the situation changes leading to a new space of designs. It provides a model of the way that designers make unexpected discoveries within their work that are useful to the design task, through expectations, and relevant to the source, as the basis for constructing the interpretation. Another experiment uses sets of growth indicators about countries as concepts. The model shows how the space of designs changes through constructive interpretation and explores the effects of salience weighting upon the construction of interpretations. The work looks towards a situated model of design: a model of design that integrates interpretation, expectation and memory into the one cognitive framework. Constructive interpretation has applications for models of analogy and computational creativity. Future work in constructive interpretation is described

A novel shape descriptor based on salient keypoints detection for binary image matching and retrieval

We introduce a shape descriptor that extracts keypoints from binary images and automatically detects the salient ones among them. The proposed descriptor operates as follows: First, the contours of the image are detected and an image transformation is used to generate background information. Next, pixels of the transformed image that have specific characteristics in their local areas are used to extract keypoints. Afterwards, the most salient keypoints are automatically detected by filtering out redundant and sensitive ones. Finally, a feature vector is calculated for each keypoint by using the distribution of contour points in its local area. The proposed descriptor is evaluated using public datasets of silhouette images, handwritten math expressions, hand-drawn diagram sketches, and noisy scanned logos. Experimental results show that the proposed descriptor compares strongly against state of the art methods, and that it is reliable when applied on challenging images such as fluctuated handwriting and noisy scanned images. Furthermore, we integrate our descripto