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This paper proposes a pooling strategy for local descriptors to produce a vector representation that is orientation-invariant yet implicitly incorporates the relative angles between features measured by their dominant orientation. This pooling is associated with a similarity metric that ensures that all the features have undergone a comparable rotation.(More)
Colorand texture are two important features in content-based image retrieval. It has been shown that using the combination of both could provide better performance. In this paper, a K-means based histogram (KBH) using the combination of color and texture features for image retrieval is proposed. Multiresolution feature vectors representing color and texture(More)
This paper studies the role and performance of local invariant features arisen from interest points in describing and sketching semantic concepts. Both the local description and spatial location of interest points are exploited, separately and jointly, for concept-based retrieval. In concept description, a visual dictionary is generated with each keyframe(More)
This letter proposes a novel co-saliency model to effectively discover and highlight co-salient objects in a set of images. Based on the gross similarity which combines color features and SIFT descriptors, some co-salient object regions are first discovered in each image as exemplars, which are exploited to generate the exemplar saliency maps with the use(More)
One of the most successful method to link all similar images within a large collection is min-Hash, which is a way to significantly speed-up the comparison of images when the underlying image representation is bag-of-words. However, the quantization step of min-Hash introduces important information loss. In this paper, we propose a generalization of(More)
Semantic Indexing Task (SIN) Run No. This paper describes the TRECVID 2011 participation of the IUPR-DFKI team in the semantic indexing task (SIN) and content based copy detection task (CCD) task. For SIN, this years participation was dominated by an significant increase of vocabulary concept size from 130 to 346 concepts. In particular the system setup has(More)
—Nearest neighbor search is known as a challenging issue that has been studied for several decades. Recently, this issue becomes more and more imminent in viewing that the big data problem arises from various fields. In this paper, a scalable solution based on hill-climbing strategy with the support of k-nearest neighbor graph (kNN) is presented. Two major(More)
In the era of big data, k-means clustering has been widely adopted as a basic processing tool in various contexts. However, its computational cost could be prohibitively high as the data size and the cluster number are large. It is well known that the processing bottleneck of k-means lies in the operation of seeking closest centroid in each iteration. In(More)
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