Semantic Indexing Task (SIN) Run No. Run ID Run Description infMAP (%) 1 F A IUPR-DFKI 1 Fisher Kernel + SVMs 2.86 2 F A IUPR-DFKI 2 Color Correlogram + SVMs 5.38 3 F A IUPR-DFKI 3 Fisher Kernel fused with Color Correlograms + SVMs 5.0 4 F A IUPR-DFKI 4 Fisher Kernel + kNN 0.71 Content-based Copy Detection (CCD) Run No. Run ID Run Description Opt.NDCR 1 *iupr-dfki.fsift F-SIFT+BoW+HE+EWGC 0.776...

Copy-move forgeries are very common image manipulations that are often carried out with malicious intents. Among the techniques devised by the Image Forensic community, those relying on SIFT features are the most effective ones. In this paper we approach the copy-move scenario from the perspective of an attacker whose goal is to remove such features. The attacks conceived so far against SIFT-ba...

Single nucleotide polymorphism (SNP) studies and random mutagenesis projects identify amino acid substitutions in protein-coding regions. Each substitution has the potential to affect protein function. SIFT (Sorting Intolerant From Tolerant) is a program that predicts whether an amino acid substitution affects protein function so that users can prioritize substitutions for further study. We hav...

SIFT is an excellent descriptor to features in medical images. However, the algorithm to achieve SIFT is tedious and takes a long time. To cope with this, we combined image integration and image region partition and proposed a modified SIFT descriptor capable of describing the global energy information of the image. We further proposed a spatial SIFT -oriented non-rigid registration model. Expe...

In this paper, we present local image descriptor using VQ-SIFT for more effective and efficient image retrieval. Instead of SIFT's weighted orientation histograms, we apply vector quantization (VQ) histogram as an alternate representation for SIFT features. Experimental results show that SIFT features using VQ-based local descriptors can achieve better image retrieval accuracy than the conventi...

SIFT (Scale Invariant Feature Transform) points are scale-space extreme points, representing local minutiae structure features in the Gaussian scale space. SIFT intensity, as a novel no-reference metric, is feasible to assess various common distortions without the access to reference images. The metric introduces image preprocessing: neighborhood enhancement through contrast enhancement of adja...

Privacy has received much attention but is still largely ignored in the multimedia community. Consider a cloud computing scenario, where the server is resource-abundant and is capable of finishing the designated tasks, it is envisioned that secure media retrieval and search with privacy-preserving will be seriously treated. In view of the fact that scaleinvariant feature transform (SIFT) has be...

This article presents a fully functional GPU-based implementation of Scale Invariant Feature Transform (SIFT) algorithm. SIFT is a popular image feature extraction algorithm. Although it is a powerful algorithm for image matching but it is also computationally very expensive. This makes it difficult to use especially in real time applications. We purpose to expedite SIFT through GPU-based imple...

Face recognition has been widely investigated in the last decade. However, real world application for face recognition is still a challenge. Most of these face recognition algorithms are under controlled settings, such as limited viewpoint and illumination changes. In this paper, we focus on face recognition which tolerates large viewpoint change. A novel framework named Lucas-Kanade Scale Inva...