A framework for learning to recognize and segment object classes using weakly supervised training data

Pantofaru, Caroline; Hebert, Martial

doi:10.5244/c.21.30

Cited by 1 publication

(1 citation statement)

References 33 publications

(51 reference statements)

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“…Therein, after an initial over-segmentation step, model-based recognition techniques are used to refine segmentation and to detect several concepts. Moreover, in [58], an object class recognition and segmentation system is presented. It is trained using weakly supervised training data, with the goal of examining the influence that different model choices can have on its performance.…”

Section: Related Workmentioning

confidence: 99%

Using Visual Context and Region Semantics for High-Level Concept Detection

Mylonas

Spyrou

Avrithis

et al. 2009

IEEE Trans. Multimedia

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Abstract-In this paper we investigate detection of high-level concepts in multimedia content through an integrated approach of visual thesaurus analysis and visual context. In the former, detection is based on model vectors that represent image composition in terms of region types, obtained through clustering over a large data set. The latter deals with two aspects, namely high-level concepts and region types of the thesaurus, employing a model of a priori specified semantic relations among concepts and automatically extracted topological relations among region types; thus it combines both conceptual and topological context. A set of algorithms is presented, which modify either the confidence values of detected concepts, or the model vectors based on which detection is performed. Visual context exploitation is evaluated on TRECVID and Corel data sets and compared to a number of related visual thesaurus approaches.

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Section: Related Workmentioning

confidence: 99%