Adding semantics to image-region annotations with the Name-It-Game

In this paper we present the Name-It-Game, an interactive multimedia game fostering the swift creation of a large data set of region-based image annotations. Compared to existing annotation games, we consider an added semantic structure, by means of the WordNet ontology, the main innovation of the Name-It-Game. Using an ontology-powered game, instead of the more traditional annotation tools, potentially makes region-based image labeling more fun and accessible for every type of user. However, the current games often present the players with hard-to-guess objects. To prevent this from happening in the Name-It-Game, we successfully identify WordNet categories which filter out hard-to-guess objects. To verify the speed of the annotation process, we compare the online Name-It-Game with a desktop tool with similar features. Results show that the Name-It-Game outperforms this tool for semantic region-based image labeling. Lastly, we measure the accuracy of the produced segmentations and compare them with carefully created LabelMe segmentations. Judging from the quantitative and qualitative results, we believe the segmentations are competitive to those of LabelMe, especially when averaged over multiple games. By adding semantics to region-based image annotations, using the Name-It-Game, we have opened up an efficient means to provide precious labels in a playful manner.     

Perceptions of the health and convenience characteristics of fresh and dried fruits

Dried fruits consumption has not received much research attention, although these foods are associated with potential health benefits. Therefore, the aim of this study was to compare fresh fruits and dried fruits on measures of health, convenience and consumer feelings.An on-line questionnaire that measured consumers' perceptions of the health benefits and convenience characteristics and their feelings about the consumption of both fresh fruits (apples and plums) and dried fruits (e.g., raisins, dates, and apricots) was completed by 693 respondents (Polish, French, and Dutch).Generally, fresh fruits were perceived to be healthier and less convenient than dried fruits. Consumers also reported more positive feelings about the consumption of fresh fruits compared to dried fruits. The more that the respondents were willing to sacrifice for their health, the more positively they rated the health aspects of both fresh and dried fruits, as well as several perceptions of convenience of both fresh and dried fruits. Health preoccupation was unrelated to most of the convenience perceptions and was associated with lower scores on several perceptions of healthiness. Recommendations for the consumer-oriented development of dried fruits are also provided.     

Multimodal Video Indexing: A Review of the State-of-the-art

Efficient and effective handling of video documents depends on the availability of indexes. Manual indexing is unfeasible for large video collections. In this paper we survey several methods aiming at automating this time and resource consuming process. Good reviews on single modality based video indexing have appeared in literature. Effective indexing, however, requires a multimodal approach in which either the most appropriate modality is selected or the different modalities are used in collaborative fashion. Therefore, instead of separately treating the different information sources involved, and their specific algorithms, we focus on the similarities and differences between the modalities. To that end we put forward a unifying and multimodal framework, which views a video document from the perspective of its author. This framework forms the guiding principle for identifying index types, for which automatic methods are found in literature. It furthermore forms the basis for categorizing these different methods.     

A phase II study of paclitaxel in heavily pretreated patients with small-cell lung cancer

Deep vein thrombosis (DVT) and pulmonary embolism (PE) are major complications associated with total knee arthroplasty. The American College of Chest Physicians recommends twice-daily, fixed-dose low-molecular-weight heparin (LMWH) as routine prophylaxis in this patient population. This study represents a cost analysis of ardeparin and enoxaparin, the two LMWHs currently available for this indication in the United States. Costs for treating DVT, PE, and major bleeding episodes were derived from values reported in the literature. Both ardeparin and enoxaparin were found to produce significant cost savings when used routinely as DVT prophylaxis after knee replacement surgery compared with no prophylaxis. Based on the currently available data, enoxaparin 40 mg once daily appears to be the least costly LMWH for routine pharmacoprophylaxis of DVT in patients undergoing knee replacement surgery.     

Repetition Estimation

International Journal of Computer Vision - Visual repetition is ubiquitous in our world. It appears in human activity (sports, cooking), animal behavior (a bee’s waggle dance), natural...     

Pointly-Supervised Action Localization

International Journal of Computer Vision - This paper strives for spatio-temporal localization of human actions in videos. In the literature, the consensus is to achieve localization by training on...     

Evaluating multimedia features and fusion for example-based event detection

Multimedia event detection (MED) is a challenging problem because of the heterogeneous content and variable quality found in large collections of Internet videos. To study the value of multimedia features and fusion for representing and learning events from a set of example video clips, we created SESAME, a system for video SEarch with Speed and Accuracy for Multimedia Events. SESAME includes multiple bag-of-words event classifiers based on single data types: low-level visual, motion, and audio features; high-level semantic visual concepts; and automatic speech recognition. Event detection performance was evaluated for each event classifier. The performance of low-level visual and motion features was improved by the use of difference coding. The accuracy of the visual concepts was nearly as strong as that of the low-level visual features. Experiments with a number of fusion methods for combining the event detection scores from these classifiers revealed that simple fusion methods, such as arithmetic mean, perform as well as or better than other, more complex fusion methods. SESAME’s performance in the 2012 TRECVID MED evaluation was one of the best reported.     

Learning Social Tag Relevance by Neighbor Voting

Social image analysis and retrieval is important for helping people organize and access the increasing amount of user tagged multimedia. Since user tagging is known to be uncontrolled, ambiguous, and overly personalized, a fundamental problem is how to interpret the relevance of a user-contributed tag with respect to the visual content the tag is describing. Intuitively, if different persons label visually similar images using the same tags, these tags are likely to reflect objective aspects of the visual content. Starting from this intuition, we propose in this paper a neighbor voting algorithm which accurately and efficiently learns tag relevance by accumulating votes from visual neighbors. Under a set of well-defined and realistic assumptions, we prove that our algorithm is a good tag relevance measurement for both image ranking and tag ranking. Three experiments on 3.5 million Flickr photos demonstrate the general applicability of our algorithm in both social image retrieval and image tag suggestion. Our tag relevance learning algorithm substantially improves upon baselines for all the experiments. The results suggest that the proposed algorithm is promising for real-world applications.