In this paper, we propose a novel approach for feature generation by appropriately fusing KAZE and SIFT features. We then use this feature set along with Minimal Complexity Machine(MCM) for object classification. We show that KAZE and SIFT features are complementary. Experimental results indicate that an elementary integration of these techniques can outperform the state-of-the-art approaches.

We propose a considerably faster approximation of the well known SIFT method. The main idea is to use efficient data structures for both, the detector and the descriptor. The detection of interest regions is considerably speed-up by using an integral image for scale space computation. The descriptor which is based on orientation histograms, is accelerated by the use of an integral orientation h...

Run No. Run ID Run Description infMAP (%) training on TV09 data (type: A) 1 IUPR-VW-TV SIFT visual words with SVMs 8.5 2 IUPR-ADAPT-TV SIFT visual words with PA1SD 5.1 combined training on YouTube and TV09 data (type: C) 3 IUPR-VW+TT-TV SIFT visual words with SVMs, fused with TubeTagger concept detection scores 8.3 4 IUPR-ADAPT-YT SIFT visual words with PA1SD, trained on YouTube, adapted to TV0...

11 Existing algorithms based on scale invariant feature transform (SIFT) and Harris corners such as edge-driven 12 dual-bootstrap iterative closest point and Harris-partial intensity invariant feature descriptor (PIIFD) respectivley have 13 been shown to be robust in registering multimodal retinal images. However, they fail to register color retinal 14 images with other modalities in the presen...

As the individual identification, access control and security appliance issues attract much attention, face recognition applications are more and more popular. The challenge of face recognition is that the performance is mainly constrained by the variations of illumination, expression, pose and accessory. And most algorithms which were proposed in recent years focused on how to conquest these c...

Partial occlusions, large pose variations, and extreme ambient illumination conditions generally cause the performance degradation of object recognition systems. Therefore, this paper presents a novel approach for fast and robust object recognition in cluttered scenes based on an improved scale invariant feature transform (SIFT) algorithm and a fuzzy closed-loop control method. First, a fast SI...

Visual content description is a key issue for machine-based image analysis and understanding. A good visual descriptor should be both discriminative enough and computationally efficient while possessing some properties of robustness to viewpoint changes and lighting condition variations. In this paper, we propose several new local descriptors based on color orthogonal local binary patterns comb...

Recognizing facial expression has remained a challenging task in computer vision. Deriving an effective facial expression recognition is an important step for successful human-computer interaction systems. This paper describes a novel approach towards facial expression recognition task. It is motivated by the success of Convolutional Neural Networks (CNN) on face recognition problems. Unlike ot...

The scale invariant feature transform (SIFT) is a widely used interest operator for supporting tasks such as 3D matching, 3D scene reconstruction, panorama stitching, image registration and motion tracking. Although SIFT is reported to be robust to disparate radiometric and geometric conditions in visible light imagery, using the default input parameters does not yield satisfactory results when...

Advancements in sequencing technologies have empowered recent efforts to identify polymorphisms and mutations on a global scale. The large number of variations and mutations found in these projects requires high-throughput tools to identify those that are most likely to have an impact on function. Numerous computational tools exist for predicting which mutations are likely to be functional, but...