Object recognition has become one of the most active research topics in computer vision in recent years. The set of features extracted from the training image is critical for good object recognition performance. The Scale Invariant Feature Transform (SIFT) was proposed by David Lowe in 1999; the SIFT features are local and effective for object recognition. In this paper we conducted a survey of...

In the light of the deep analyses of subspace recognition and SIFT recognition, a novel image recognition based on subspace and SIFT is proposed to provide a recognition from global features to minutiae features. First, subspace is used to implement coarse image recognition, gaining one or more candidate samples with different identities. Then, a special SIFT recognition environment is designed...

In this paper, we present a novel color independent components based SIFT descriptor (termed CIC-SIFT) for object/scene classification. We first learn an efficient color transformation matrix based on independent component analysis (ICA), which is adaptive to each category in a database. The ICA-based color transformation can enhance contrast between the objects and the background in an image. ...

In the conventional Bag-of-Features (BoF) model for image classification, handcrafted descriptors such as SIFT are used for local patch description. Since SIFT is not flipping invariant, left-right flipping operation on images might harm the classification accuracy. To deal with, some algorithms augmented the training and testing datasets with flipped image copies. These models produce better c...

Few distributed software-implemented fault tolerance (SIFT) environments have been experimentally evaluated using substantial applications to show that they protect both themselves and the applications from errors. This paper presents an experimental evaluation of a SIFT environment used to oversee spaceborne applications as part of the Remote Exploration and Experimentation (REE) program at th...

Scale-invariant feature transform (SIFT) was an algorithm in computer vision to detect and describe local features in images. Due to its excellent performance, SIFT was widely used in many applications, but the implementation of SIFT was complicated and time-consuming. To solve this problem, this paper presented a novel acceleration algorithm for SIFT implementation based on Compute Unified Dev...

This paper first presents a new oRGB-SIFT descriptor, and then integrates it with other color SIFT features to produce the novel Color SIFT Fusion (CSF) and the Color Grayscale SIFT Fusion (CGSF) descriptors for image classification with special applications to biometrics. Classification is implemented using a novel EFM-KNN classifier, which combines the Enhanced Fisher Model (EFM) and the K Ne...

The large-scale video data on the web contain a lot of semantics, which are an important part of semantic web. Video descriptors can usually represent somewhat the semantics. Thus, they play a very important role in web multimedia content analysis, such as Scale-invariant feature transform (SIFT) feature. In this paper, we proposed a new video descriptor, called a temporalcompress and shorter S...

Feature-based image matching is one of the most fundamental issues in computer vision tasks. As the number of features increases, the matching process rapidly becomes a bottleneck. This paper presents a novel method to speed up SIFT feature matching. The main idea is to extend SIFT feature by a few pairwise independent angles, which are invariant to rotation, scale and illumination changes. Dur...