Semantic spaces revisited: investigating the performance of auto-annotation and semantic retrieval using semantic spaces

Semantic spaces encode similarity relationships between objects as a function of position in a mathematical space. This paper discusses three different formulations for building semantic spaces which allow the automatic-annotation and semantic retrieval of images. The models discussed in this paper require that the image content be described in the form of a series of visual-terms, rather than as a continuous feature-vector. The paper also discusses how these term-based models compare to the latest state-of-the-art continuous feature models for auto-annotation and retrieval

On Acquisition and Analysis of a Dataset Comprising of Gait, Ear and Semantic data

In outdoor scenarios such as surveillance where there is very little control over the environments, complex computer vision algorithms are often required for analysis. However constrained environments, such as walkways in airports where the surroundings and the path taken by individuals can be controlled, provide an ideal application for such systems. Figure 1.1 depicts an idealised constrained environment. The path taken by the subject is restricted to a narrow path and once inside is in a volume where lighting and other conditions are controlled to facilitate biometric analysis. The ability to control the surroundings and the flow of people greatly simplifes the computer vision task, compared to typical unconstrained environments. Even though biometric datasets with greater than one hundred people are increasingly common, there is still very little known about the inter and intra-subject variation in many biometrics. This information is essential to estimate the recognition capability and limits of automatic recognition systems. In order to accurately estimate the inter- and the intra- class variance, substantially larger datasets are required [40]. Covariates such as facial expression, headwear, footwear type, surface type and carried items are attracting increasing attention; although considering the potentially large impact on an individuals biometrics, large trials need to be conducted to establish how much variance results. This chapter is the first description of the multibiometric data acquired using the University of Southampton's Multi-Biometric Tunnel [26, 37]; a biometric portal using automatic gait, face and ear recognition for identification purposes. The tunnel provides a constrained environment and is ideal for use in high throughput security scenarios and for the collection of large datasets. We describe the current state of data acquisition of face, gait, ear, and semantic data and present early results showing the quality and range of data that has been collected. The main novelties of this dataset in comparison with other multi-biometric datasets are: 1. gait data exists for multiple views and is synchronised, allowing 3D reconstruction and analysis; 2. the face data is a sequence of images allowing for face recognition in video; 3. the ear data is acquired in a relatively unconstrained environment, as a subject walks past; and 4. the semantic data is considerably more extensive than has been available previously. We shall aim to show the advantages of this new data in biometric analysis, though the scope for such analysis is considerably greater than time and space allows for here

Detection of Social Events in Streams of Social Multimedia

Combining items from social media streams, such as Flickr photos and Twitter tweets, into meaningful groups can help users contextualise and consume more effectively the torrents of information continuously being made available on the social web. This task is made challenging due to the scale of the streams and the inherently multimodal nature of the information being contextualised.The problem of grouping social media items into meaningful groups can be seen as an ill-posed and application specific unsupervised clustering problem. A fundamental question in multimodal contexts is determining which features best signify that two items should belong to the same grouping.This paper presents a methodology which approaches social event detection as a streaming multi-modal clustering task. The methodology takes advantage of the temporal nature of social events and as a side benefit, allows for scaling to real-world datasets. Specific challenges of the social event detection task are addressed: the engineering and selection of the features used to compare items to one another; a feature fusion strategy that incorporates relative importance of features; the construction of a single sparse affinity matrix; and clustering techniques which produce meaningful item groups whilst scaling to cluster very large numbers of items.The state-of-the-art approach presented here is evaluated using the ReSEED dataset with standardised evaluation measures. With automatically learned feature weights, we achieve an F1 score of 0.94, showing that a good compromise between precision and recall of clusters can be achieved. In a comparison with other state-of-the-art algorithms our approach is shown to give the best results

Experiments in Diversifying Flickr Result Sets

The 2013 MediaEval Retrieving Diverse Social Images Task looked to tackling the problem of search result diversification of Flickr results sets formed from queries about geographic places and landmarks. In this paper we describe our approach of using a min-max similarity diversifier coupled with pre-filters and a reranker. We also demonstrate a number of novel features for measuring similarity to use in the diversification step

An investigation of techniques that aim to improve the quality of labels provided by the crowd

The 2013 MediaEval Crowdsourcing task looked at the problem of working with noisy crowdsourced annotations of image data. The aim of the task was to investigate possible techniques for estimating the true labels of an image by using the set of noisy crowdsourced labels, and possibly any content and metadata from the image itself. For the runs in this paper, we’ve applied a shotgun approach and tried a number of existing techniques, which include generative probabilistic models and further crowdsourcing