No abstract

In this paper we study the problem of the detection of semantic objects from known categories in images. Unlike existing techniques which operate at the pixel or at a patch level for recognition, we propose to rely on the categorization of image segments. Recent work has highlighted that image segments provide a sound support for visual object class recognition. In this work, we use image segments as primitives to extract robust features and train detection models for a predefined set of categories. Several segmentation algorithms are benchmarked and their performances for segment recognition are compared. We then propose two methods for enhancing the segments classification, one based on the fusion of the classification results obtained with the different segmentations, the other one based on the optimization of the global labelling by correcting local ambiguities between neighbor segments. We use as a benchmark the Microsoft MSRC-21 image database and show that our method competes with the current state-of-the-art.

The paper addresses the problem of object search in video content. Both Query-By-Example paradigm and context search are explored. In QBE paradigm the object of interest is searched by matching of object signatures built from SURF descriptors with on-the-fly computed signatures in frames. The "context" search is understood as a query on the whole frame with features extracted after a region-based segmentation. Both kinds of features are transcribed in Bag-Of-Words framework. The combination of Bag-of-Visual-Words and Bag-of-RegionWords gives promising results in TRECVID'2011 Instance Search Task.

No abstract

No abstract