Retina-Enhanced SURF Descriptors for Semantic Concept Detection in Videos

International audienceThis paper proposes to investigate the potential benefit of the use of low-level human vision behaviors in the context of high-level semantic concept detection. A large part of the current approaches relies on the Bag-of-Words (BoW) model, which has proven itself to be a good choice especially for object recognition in images. Its extension from static images to video sequences exhibits some new problems to cope with, mainly the way to use the added temporal dimension for detecting the target concepts (swimming, drinking...). In this study, we propose to apply a human retina model to preprocess video sequences, before constructing a State-Of-The-Art BoW analysis. This preprocessing, designed in a way that enhances the appearance especially of static image elements, increases the performance by introducing robustness to traditional image and video problems, such as luminance variation, shadows, compression artifacts and noise. These approaches are valuated on the TrecVid 2010 Semantic Indexing task datasets, containing 130 high-level semantic concepts. We consider the well-known SURF descriptor as the entry point of the BoW system, but this work could be extended to any other local gradient based descriptor

IRIM at TRECVID 2012: Semantic Indexing and Instance Search

International audienceThe IRIM group is a consortium of French teams work- ing on Multimedia Indexing and Retrieval. This paper describes its participation to the TRECVID 2012 se- mantic indexing and instance search tasks. For the semantic indexing task, our approach uses a six-stages processing pipelines for computing scores for the likeli- hood of a video shot to contain a target concept. These scores are then used for producing a ranked list of im- ages or shots that are the most likely to contain the tar- get concept. The pipeline is composed of the following steps: descriptor extraction, descriptor optimization, classi cation, fusion of descriptor variants, higher-level fusion, and re-ranking. We evaluated a number of dif- ferent descriptors and tried di erent fusion strategies. The best IRIM run has a Mean Inferred Average Pre- cision of 0.2378, which ranked us 4th out of 16 partici- pants. For the instance search task, our approach uses two steps. First individual methods of participants are used to compute similrity between an example image of in- stance and keyframes of a video clip. Then a two-step fusion method is used to combine these individual re- sults and obtain a score for the likelihood of an instance to appear in a video clip. These scores are used to ob- tain a ranked list of clips the most likely to contain the queried instance. The best IRIM run has a MAP of 0.1192, which ranked us 29th on 79 fully automatic runs

IRIM at TRECVID 2013: Semantic Indexing and Instance Search

International audienceThe IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes its participation to the TRECVID 2013 semantic indexing and instance search tasks. For the semantic indexing task, our approach uses a six-stages processing pipelines for computing scores for the likelihood of a video shot to contain a target concept. These scores are then used for producing a ranked list of images or shots that are the most likely to contain the target concept. The pipeline is composed of the following steps: descriptor extraction, descriptor optimization, classiffication, fusion of descriptor variants, higher-level fusion, and re-ranking. We evaluated a number of different descriptors and tried different fusion strategies. The best IRIM run has a Mean Inferred Average Precision of 0.2796, which ranked us 4th out of 26 participants

Learned features versus engineered features for semantic video indexing

International audienceIn this paper, we compare "traditional" engineered (hand-crafted) features (or descriptors) and learned features for content-based semantic indexing of video documents. Learned (or semantic) features are obtained by training classifiers for other target concepts on other data. These classifiers are then applied to the current collection. The vector of classification scores is the new feature used for training a classifier for the current target concepts on the current collection. If the classifiers used on the other collection are of the Deep Convolutional Neural Network (DCNN) type, it is possible to use as a new feature not only the score values provided by the last layer but also the intermediate values corresponding to the output of all the hidden layers. We made an extensive comparison of the performance of such features with traditional engineered ones as well as with combinations of them. The comparison was made in the context of the TRECVid semantic indexing task. Our results confirm those obtained for still images: features learned from other training data generally outperform engineered features for concept recognition. Additionally, we found that directly training SVM classifiers using these features does significantly better than partially retraining the DCNN for adapting it to the new data. We also found that, even though the learned features performed better that the engineered ones, the fusion of both of them perform significantly better, indicating that engineered features are still useful, at least in this case

Gaze Guidance, Task-Based Eye Movement Prediction, and Real-World Task Inference using Eye Tracking

The ability to predict and guide viewer attention has important applications in computer graphics, image understanding, object detection, visual search and training. Human eye movements provide insight into the cognitive processes involved in task performance and there has been extensive research on what factors guide viewer attention in a scene. It has been shown, for example, that saliency in the image, scene context, and task at hand play significant roles in guiding attention. This dissertation presents and discusses research on visual attention with specific focus on the use of subtle visual cues to guide viewer gaze and the development of algorithms to predict the distribution of gaze about a scene. Specific contributions of this work include: a framework for gaze guidance to enable problem solving and spatial learning, a novel algorithm for task-based eye movement prediction, and a system for real-world task inference using eye tracking. A gaze guidance approach is presented that combines eye tracking with subtle image-space modulations to guide viewer gaze about a scene. Several experiments were conducted using this approach to examine its impact on short-term spatial information recall, task sequencing, training, and password recollection. A model of human visual attention prediction that uses saliency maps, scene feature maps and task-based eye movements to predict regions of interest was also developed. This model was used to automatically select target regions for active gaze guidance to improve search task performance. Finally, we develop a framework for inferring real-world tasks using image features and eye movement data. Overall, this dissertation naturally leads to an overarching framework, that combines all three contributions to provide a continuous feedback system to improve performance on repeated visual search tasks. This research has important applications in data visualization, problem solving, training, and online education

Adding Cues to Binary Feature Descriptors for Visual Place Recognition

In this paper we propose an approach to embed continuous and selector cues in binary feature descriptors used for visual place recognition. The embedding is achieved by extending each feature descriptor with a binary string that encodes a cue and supports the Hamming distance metric. Augmenting the descriptors in such a way has the advantage of being transparent to the procedure used to compare them. We present two concrete applications of our methodology, demonstrating the two considered types of cues. In addition to that, we conducted on these applications a broad quantitative and comparative evaluation covering five benchmark datasets and several state-of-the-art image retrieval approaches in combination with various binary descriptor types.Comment: 8 pages, 8 figures, source: www.gitlab.com/srrg-software/srrg_bench, submitted to ICRA 201