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

Finding Semantically Related Videos in Closed Collections

Modern newsroom tools offer advanced functionality for automatic and semi-automatic content collection from the web and social media sources to accompany news stories. However, the content collected in this way often tends to be unstructured and may include irrelevant items. An important step in the verification process is to organize this content, both with respect to what it shows, and with respect to its origin. This chapter presents our efforts in this direction, which resulted in two components. One aims to detect semantic concepts in video shots, to help annotation and organization of content collections. We implement a system based on deep learning, featuring a number of advances and adaptations of existing algorithms to increase performance for the task. The other component aims to detect logos in videos in order to identify their provenance. We present our progress from a keypoint-based detection system to a system based on deep learning

Deliverable D1.6 Intelligent hypervideo analysis evaluation, final results

This deliverable describes the conducted evaluation activities for assessing the performance of a number of developed methods for intelligent hypervideo analysis and the usability of the implemented Editor Tool for supporting video annotation and enrichment. Based on the performance evaluations reported in D1.4 regarding a set of LinkedTV analysis components, we extended our experiments for assessing the effectiveness of newer versions of these methods as well as of entirely new techniques, concerning the accuracy and the time efficiency of the analysis. For this purpose, in-house experiments and participations at international benchmarking activities were made, and the outcomes are reported in this deliverable. Moreover, we present the results of user trials regarding the developed Editor Tool, where groups of experts assessed its usability and the supported functionalities, and evaluated the usefulness and the accuracy of the implemented video segmentation approaches based on the analysis requirements of the LinkedTV scenarios. By this deliverable we complete the reporting of WP1 evaluations that aimed to assess the efficiency of the developed multimedia analysis methods throughout the project, according to the analysis requirements of the LinkedTV scenarios

LIG at TRECVid 2014: Semantic Indexing

International audienceLIG participated to the semantic indexing main task. LIG also participated to the organization of this task. This paper describes these participations which are quite similar to our previous year's participations (within the Quaero consortium).For the semantic indexing main 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, classification, fusion of descriptor variants , higher-level fusion, and re-ranking. We used a number of different descriptors and a hierarchical fusion strategy. We also used conceptual feedback by adding a vector of classification score to the pool of de-scriptors. The main innovation this year consisted in the inclusion of semantic descriptors computed using a deep learning method. We also used the uploader field available in the metadata and this did lead to a small improvement. The best LIG run has a Mean Inferred Average Precision of 0.2659, which ranked us 4th out of 15 participants