Task-based annotation and retrieval for image information management

Continuing advances in digital image capture and storage are resulting in a proliferation of imagery and associated problems of information overload in image domains. In this work we present a framework that supports image management using an interactive approach that captures and reuses task-based contextual information. Our framework models the relationship between images and domain tasks they support by monitoring the interactive manipulation and annotation of task-relevant imagery. During image analysis, interactions are captured and a task context is dynamically constructed so that human expertise, proficiency and knowledge can be leveraged to support other users in carrying out similar domain tasks using case-based reasoning techniques. In this article we present our framework for capturing task context and describe how we have implemented the framework as two image retrieval applications in the geo-spatial and medical domains. We present an evaluation that tests the efficiency of our algorithms for retrieving image context information and the effectiveness of the framework for carrying out goal-directed image tasks. © 2010 Springer Science+Business Media, LLC.

Exploring demand and production planning challenges in the food processing industry:a case study

This paper explores demand and production management challenges in the food processing industry. The goal is to identify the main production planning constraints and secondly to explore how each of these constraints affects company’s performance in terms of costs and customer service level. A single case study methodology was preferred since it enabled the collection of in-depth data. Findings suggest that product shelf life, carcass utilization and production lead time are the main constraints affecting supply chain efficiency and hence, a single planning approach is not appropriate when different products have different technological and processing characteristics.

Psychological cue use and implications for a clinical decision support system

Effective clinical decision making depends upon identifying possible outcomes for a patient, selecting relevant cues, and processing the cues to arrive at accurate judgements of each outcome's probability of occurrence. These activities can be considered as classification tasks. This paper describes a new model of psychological classification that explains how people use cues to determine class or outcome likelihoods. It proposes that clinicians respond to conditional probabilities of outcomes given cues and that these probabilities compete with each other for influence on classification. The model explains why people appear to respond to base rates inappropriately, thereby overestimating the occurrence of rare categories, and a clinical example is provided for predicting suicide risk. The model makes an effective representation for expert clinical judgements and its psychological validity enables it to generate explanations in a form that is comprehensible to clinicians. It is a strong candidate for incorporation within a decision support system for mental-health risk assessment, where it can link with statistical and pattern recognition tools applied to a database of patients. The symbiotic combination of empirical evidence and clinical expertise can provide an important web-based resource for risk assessment, including multi-disciplinary education and training. © 2002 Informa UK Ltd All rights reserved.

Analysis and representation of statistical performance of JPEG2000 encoded image over wireless channels

JPEG2000 is a new coming image standard. In this paper we analyze the performance of error resilience tools in JPEG2000, and present an analytical model to estimate the quality of JPEG2000 encoded image transmitted over wireless channels. The effectiveness of the analytical model is validated by simulation results. Furthermore, analytical model is utilized by the base station to design efficient unequally error protection schemes for JPEG2000 transmission. In the design, a utility function is denned to make a tradeoff between the image quality and the cost for transmitting the image over wireless channel. © 2002 IEEE.

False recognition of objects in visual scenes:findings from a combined direct and indirect memory test

We report an extension of the procedure devised by Weinstein and Shanks (Memory & Cognition 36:1415-1428, 2008) to study false recognition and priming of pictures. Participants viewed scenes with multiple embedded objects (seen items), then studied the names of these objects and the names of other objects (read items). Finally, participants completed a combined direct (recognition) and indirect (identification) memory test that included seen items, read items, and new items. In the direct test, participants recognized pictures of seen and read items more often than new pictures. In the indirect test, participants' speed at identifying those same pictures was improved for pictures that they had actually studied, and also for falsely recognized pictures whose names they had read. These data provide new evidence that a false-memory induction procedure can elicit memory-like representations that are difficult to distinguish from "true" memories of studied pictures. © 2012 Psychonomic Society, Inc.

A stable graph-based representation for object recognition through high-order matching

Many Object recognition techniques perform some flavour of point pattern matching between a model and a scene. Such points are usually selected through a feature detection algorithm that is robust to a class of image transformations and a suitable descriptor is computed over them in order to get a reliable matching. Moreover, some approaches take an additional step by casting the correspondence problem into a matching between graphs defined over feature points. The motivation is that the relational model would add more discriminative power, however the overall effectiveness strongly depends on the ability to build a graph that is stable with respect to both changes in the object appearance and spatial distribution of interest points. In fact, widely used graph-based representations, have shown to suffer some limitations, especially with respect to changes in the Euclidean organization of the feature points. In this paper we introduce a technique to build relational structures over corner points that does not depend on the spatial distribution of the features. © 2012 ICPR Org Committee.

The influence of variations in eating disorder-related symptoms on processing of emotional faces in a non-clinical female sample:an eye-tracking study

This study aimed to: i) determine if the attention bias towards angry faces reported in eating disorders generalises to a non-clinical sample varying in eating disorder-related symptoms; ii) examine if the bias occurs during initial orientation or later strategic processing; and iii) confirm previous findings of impaired facial emotion recognition in non-clinical disordered eating. Fifty-two females viewed a series of face-pairs (happy or angry paired with neutral) whilst their attentional deployment was continuously monitored using an eye-tracker. They subsequently identified the emotion portrayed in a separate series of faces. The highest (n=18) and lowest scorers (n=17) on the Eating Disorders Inventory (EDI) were compared on the attention and facial emotion recognition tasks. Those with relatively high scores exhibited impaired facial emotion recognition, confirming previous findings in similar non-clinical samples. They also displayed biased attention away from emotional faces during later strategic processing, which is consistent with previously observed impairments in clinical samples. These differences were related to drive-for-thinness. Although we found no evidence of a bias towards angry faces, it is plausible that the observed impairments in emotion recognition and avoidance of emotional faces could disrupt social functioning and act as a risk factor for the development of eating disorders.

To move or not to move, that is the question! Body schema and non-action oriented body representations:an fMRI meta-analytic study

Many studies have attempted to identify the different cognitive components of body representation (BR). Due to methodological issues, the data reported in these studies are often confusing. Here we summarize the fMRI data from previous studies and explore the possibility of a neural segregation between BR supporting actions (body-schema, BS) or not (non-oriented-to-action-body-representation, NA). We performed a general activation likelihood estimation meta-analysis of 59 fMRI experiments and two individual meta-analyses to identify the neural substrates of different BR. Body processing involves a wide network of areas in occipital, parietal, frontal and temporal lobes. NA selectively activates the somatosensory primary cortex and the supramarginal gyrus. BS involves the primary motor area and the right extrastriate body area. Our data suggest that motor information and recognition of body parts are fundamental to build BS. Instead, sensory information and processing of the egocentric perspective are more important for NA. In conclusion, our results strongly support the idea that different and segregated neural substrates are involved in body representations orient or not to actions.

Edge centrality via the Holevo quantity

In the study of complex networks, vertex centrality measures are used to identify the most important vertices within a graph. A related problem is that of measuring the centrality of an edge. In this paper, we propose a novel edge centrality index rooted in quantum information. More specifically, we measure the importance of an edge in terms of the contribution that it gives to the Von Neumann entropy of the graph. We show that this can be computed in terms of the Holevo quantity, a well known quantum information theoretical measure. While computing the Von Neumann entropy and hence the Holevo quantity requires computing the spectrum of the graph Laplacian, we show how to obtain a simplified measure through a quadratic approximation of the Shannon entropy. This in turns shows that the proposed centrality measure is strongly correlated with the negative degree centrality on the line graph. We evaluate our centrality measure through an extensive set of experiments on real-world as well as synthetic networks, and we compare it against commonly used alternative measures.

The average mixing matrix signature

Laplacian-based descriptors, such as the Heat Kernel Signature and the Wave Kernel Signature, allow one to embed the vertices of a graph onto a vectorial space, and have been successfully used to find the optimal matching between a pair of input graphs. While the HKS uses a heat di↵usion process to probe the local structure of a graph, the WKS attempts to do the same through wave propagation. In this paper, we propose an alternative structural descriptor that is based on continuoustime quantum walks. More specifically, we characterise the structure of a graph using its average mixing matrix. The average mixing matrix is a doubly-stochastic matrix that encodes the time-averaged behaviour of a continuous-time quantum walk on the graph. We propose to use the rows of the average mixing matrix for increasing stopping times to develop a novel signature, the Average Mixing Matrix Signature (AMMS). We perform an extensive range of experiments and we show that the proposed signature is robust under structural perturbations of the original graphs and it outperforms both the HKS and WKS when used as a node descriptor in a graph matching task.