Incremental concept learning with few training examples and hierarchical classification

Object recognition and localization are important to automatically interpret video and allow better querying on its content. We propose a method for object localization that learns incrementally and addresses four key aspects. Firstly, we show that for certain applications, recognition is feasible with only a few training samples. Secondly, we show that novel objects can be added incrementally without retraining existing objects, which is important for fast interaction. Thirdly, we show that an unbalanced number of positive training samples leads to biased classi er scores that can be corrected by modifying weights. Fourthly, we show that the detector performance can deteriorate due to hard-negative mining for similar or closely related classes (e.g., for Barbie and dress, because the doll is wearing a dress). This can be solved by our hierarchical classi cation. We introduce a new dataset, which we call TOSO, and use it to demonstrate the e ectiveness of the proposed method for the localization and recognition of multiple objects in images.This research was performed in the GOOSE project, which is jointly funded by the enabling technology program Adaptive Multi Sensor Networks (AMSN) and the MIST research program of the Dutch Ministry of Defense. This publication was supported by the research program Making Sense of Big Data (MSoBD).peer-reviewe

TNO at TRECVID 2013 : multimedia event detection and instance search

We describe the TNO system and the evaluation results for TRECVID 2013 Multimedia Event Detection (MED) and instance search (INS) tasks. The MED system consists of a bag-of-word (BOW) approach with spatial tiling that uses low-level static and dynamic visual features, an audio feature and high-level concepts. Automatic speech recognition (ASR) and optical character recognition (OCR) are not used in the system. In the MED case with 100 example training videos, support-vector machines (SVM) are trained and fused to detect an event in the test set. In the case with 0 example videos, positive and negative concepts are extracted as keywords from the textual event description and events are detected with the high-level concepts. The MED results show that the SIFT keypoint descriptor is the one which contributes best to the results, fusion of multiple low-level features helps to improve the performance, and the textual event-description chain currently performs poorly. The TNO INS system presents a baseline open-source approach using standard SIFT keypoint detection and exhaustive matching. In order to speed up search times for queries a basic map-reduce scheme is presented to be used on a multi-node cluster. Our INS results show above-median results with acceptable search times.This research for the MED submission was performed in the GOOSE project, which is jointly funded by the enabling technology program Adaptive Multi Sensor Networks (AMSN) and the MIST research program of the Dutch Ministry of Defense. The INS submission was partly supported by the MIME project of the creative industries knowledge and innovation network CLICKNL.peer-reviewe

Complete enumeration of pure-level and mixed-level orthogonal arrays

We specify an algorithm to enumerate a minimum complete set of combinatorially non-isomorphic orthogonal arrays of given strength t, run-size N, and level-numbers of the factors. The algorithm is the first one handling general mixed-level and pure-level cases. Using an implementation in C, we generate most non-trivial series for t = 2, N≥28, t = 3, N≥64, and t = 4,N≥168. The exceptions define limiting run-sizes for which the algorithm returns complete sets in a reasonable amount of time

Threat detection in tweets with trigger patterns and contextual cues

Abstract-Many threats in the real world can be related to activities in open sources on the internet. Early detection of threats based on internet information could assist in the prevention of incidents. However, the amount of data in social media, blogs and forums rapidly increases and it is time consuming for security services to monitor all these open sources. Therefore, it is important to have a system that automatically ranks messages based on their threat potential and thereby allows security operators to check these messages more efficiently. In this paper, we present a novel method for detecting threatening messages on Twitter based on trigger keywords and contextual cues. The system was tested on multiple large collections of Dutch tweets. Our experimental results show that our system can successfully analyze messages and recognize threatening content

Improved depth perception with three-dimensional auxiliary display and computer generated three-dimensional panoramic overviews in robot-assisted laparoscopy

In comparison to open surgery, endoscopic surgery offers impaired depth perception and narrower field-of-view. To improve depth perception, the Da Vinci robot offers three-dimensional (3-D) video on the console for the surgeon but not for assistants, although both must collaborate. We improved the shared perception of the whole surgical team by connecting live 3-D monitors to all three available Da Vinci generations, probed user experience after two years by questionnaire, and compared time measurements of a predefined complex interaction task performed with a 3-D monitor versus two-dimensional. Additionally, we investigated whether the complex mental task of reconstructing a 3-D overview from an endoscopic video can be performed by a computer and shared among users. During the study, 925 robot-assisted laparoscopic procedures were performed in three hospitals, including prostatectomies, cystectomies, and nephrectomies. Thirty-one users participated in our questionnaire. Eighty-four percent preferred 3-D monitors and 100% reported spatial-perception improvement. All participating urologists indicated quicker performance of tasks requiring delicate collaboration (e.g., clip placement) when assistants used 3-D monitors. Eighteen users participated in a timing experiment during a delicate cooperation task in vitro. Teamwork was significantly (40%) faster with the 3-D monitor. Computer-generated 3-D reconstructions from recordings offered very wide interactive panoramas with educational value, although the present embodiment is vulnerable to movement artifacts