Précision et rappel

Résumé

vignette|350px|Précision et rappel (« recall »). La précision compte la proportion d'items pertinents parmi les items sélectionnés alors que le rappel compte la proportion d'items pertinents sélectionnés parmi tous les items pertinents sélectionnables. Dans les domaines de la reconnaissance de formes, de la recherche d'information et de la classification automatique, la précision (ou valeur prédictive positive) est la proportion des items pertinents parmi l'ensemble des items proposés ; le rappel (ou sensibilité) est la proportion des items pertinents proposés parmi l'ensemble des items pertinents. Ces deux notions correspondent ainsi à une conception et à une mesure de la pertinence. Lorsqu'un moteur de recherche, par exemple, retourne 30 pages web dont seulement 20 sont pertinentes (les vrais positifs) et 10 ne le sont pas (les faux positifs), mais qu'il omet 40 autres pages pertinentes (les faux négatifs), sa précision est de 20/(20+10) = 2/3 et son rappel

Source officielle

https://fr.wikipedia.org/wiki/Pr%C3%A9cision_et_rappel

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Integrating visual and semantic descriptions for effective, flexible and user-friendly image retrieval

Zoran Pecenovic

Effectively managing a large collection of multimedia documents is a challenge, addressed by many disciplines from signal processing through database systems to artificial intelligence and interaction design. The problems to be solved have rarely been considered together. We propose a series of novel solutions for: the system architecture, the document content characterization, the retrieval methodology, and the user interaction schemes. We propose and describe a communicating components architecture using a new open and flexible protocol for messaging. Its foundation, the multimedia retrieval markup language (mrml) is specified with examples of the benefits of this approach. The general multimedia application domain is restricted mainly to image documents that carry semantic annotation like captions or meta-data. The integration of perceptual and semantic content descriptions into a single retrieval structure is the principal contribution of our work. The proposed method allows for improved effectiveness, augmented functionality and high flexibility for the extension to other media types like audio or video. The relationships among the two different representational characteristics can be exploited for more effective retrieval and other tasks like semi-automatic image annotation or illustration of semantic concepts. The final contribution is the study and implementation of user interaction metaphors that are intuitive and attempt to further bridge the gap between user's and system's notion of relevance. A rich set of information need formulation tools is proposed to users of varying skill and of varying requirements. Merging query specification with result visualization and browsing, using interactively explorable search spaces, offers a single access point to most retrieval tasks.

EPFL2003

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, ,

Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. In this paper, we introduce a method for salient region detection that outputs full resolution saliency maps with well-defined boundaries of salient objects. These boundaries are preserved by retaining substantially more frequency content from the original image than other existing techniques. Our method exploits features of color and luminance, is simple to implement, and is computationally efficient. We compare our algorithm to five state-of-the-art salient region detection methods with a frequency domain analysis, ground truth, and a salient object segmentation application. Our method outperforms the five algorithms both on the ground-truth evaluation and on the segmentation task by achieving both higher precision and better recall.

2009

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Real-time workload estimation can be an important tool to improve human-machine interaction. Despite multiple efforts in this sense, most studies focused on tasks designed to have extreme conditions of workload (high vs low), and these levels remain rather constant throughout task execution. However, some applications may induce changing levels of workload and it is not clear how decoders trained in the extreme conditions will perform. In this study we study this scenario in a simulated drone task where participants had to fly through several waypoints. The task difficulty was modulated by the size of waypoints; in one of the studied conditions, the size was modulated in real time by the EEG-based decoding of the perceived difficulty. We show that this protocol can effectively induce different levels of workload and perceived difficulty. Furthermore, post-experiment analysis using a sparse-regularized technique supports the feasibility of decoding perceived difficulty in this dynamically changing task above chance level (average class-balanced accuracy across subjects 67% ± 7%). Interestingly, an analysis of the selected features in independent recording sessions showed that roughly 20% of the features were stable across multiple sessions.

2018

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