International Conference Papers
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Item Object Detection in Images Based on Homogeneous Region Segmentation(Springer, 2018) Amrane, Abdesalam; Meziane, Abdelkrim; Boulkrinat, Nour El HoudaImage segmentation for object detection is one of the most fundamental problems in computer vision, especially in object-region extraction task. Most popular approaches in the segmentation/object detection tasks use sliding-window or super-pixel labeling methods. The first method suffers from the number of window proposals, whereas the second suffers from the over-segmentation problem. To overcome these limitations, we present two strategies: the first one is a fast algorithm based on the region growing method for segmenting images into homogeneous regions. In the second one, we present a new technique for similar region merging, based on a three similarity measures, and computed using the region adjacency matrix. All of these methods are evaluated and compared to other state-of-the-art approaches that were applied on the Berkeley image database. The experimentations yielded promising results and would be used for future directions in our work.Item Indexing multimedia content for textual querying: A multimodal approach(2013-07) Amrane, Abdesalam; Mellah, Hakima; Amghar, Youssef; Aliradi, RachidMultimedia retrieval approaches are classified into three categories: those using textual information, and those using low-level information and those that combine different information extracted from multimedia. Each approach has its advantages and disadvantages as well to improving multimedia retrieval systems. The recent works are oriented towards multimodal approaches. It is in this context that we propose an approach that combines the surrounding text with the information extracted from the visual content of multimedia and represented in the same repository in order to allow querying multimedia content based on keywords or concepts. Each word contained in queries or in description of multimedia is disambiguated by using the WordNet in order to define its semantic concept.