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XRCE’s Participation at Medical Image Modality Classification and Ad-hoc Retrieval Tasks of ImageCLEF 2011
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| Gabriela Csurka, Stéphane Clinchant, Guillaume Jacquet |
| Conference on Multilingual and Multimodal Information Access Evaluation, Amsterdam, Netherlands, 19-22 September 2011. |
The aim of this document is to describe our methods used in the Medical Image Modality Classification and Ad-hoc Image Retrieval Tasks of ImageClef 2011. The main novelty in medical image modality classification this year was, that there were more classes (18 modalities) organized in a hierarchy and for some categories only few annotated examples were available. Therefore, our strategy in image categorization was to use a semi-supervised approach. In our experiments, we investigated mono-modal (text and image) and mixed modality based classification. The image classification was based on Fisher Vectors built on SIFT-like local orientation histograms and local color statistics. For text representation we used a binarized bag-ofwords representation where each element indicated whether the term appeared in the image caption or not. In the case of multi-modal classification, we simply averaged the text and image classification scores.
For the ad-hoc retrieval task, we used the image captions for text retrieval and Fisher Vectors for visual similarity and modality detection. Our text runs were based on a late fusion of different state of the art text experts and the Lexical Entailment model. This Lexical Entailement model used the last year articles to compute similarities between terms and rank first at the previous challenge.
Concerning the submitted runs, we realized that we forgot by inadvertance3, to submit our best run from last year [3]. We did not submit either improvement over this run, which was proposed in [6]. Overall, this explain the medium performance of our submitted runs. In this document, we show that our system from last year and its improvements would have achieve top performance.We have not tuned the parameter of this system for this year task, we have just evaluated the runs we did not submit !.
Finally, we experimented with different fusion strategies of our textual expert, visual expert and image modality classification scores, which gives consistent results to last year results and to our analysis presented in [6].
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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