SIFT Feature with Relevance Feedback for Image Retrieval Written by Administrator Wednesday, 16 March 2011 08:52 Last Updated Monday, 21 March 2011 07:32 In this paper, we used the Scale Invariant Feature Transform (SIFT) feature for image retrieval. SIFT descriptors are invariant to image scaling, transformation,rotation and partially invariant to illumination changes and affine, gives the loc...

It has been argued that analogy is the core of cognition. In AI research, algorithms for analogy are often limited by the need for hand-coded highlevel representations as input. An alternative approach is to use high-level perception, in which high-level representations are automatically generated from raw data. Analogy perception is the process of recognizing analogies using high-level percept...

This paper presents a novel approach to sign language recognition that provides extremely high classification rates on minimal training data. Key to this approach is a 2 stage classification procedure where an initial classification stage extracts a high level description of hand shape and motion. This high level description is based upon sign linguistics and describes actions at a conceptual l...

SIFT Feature with Relevance Feedback for Image Retrieval Written by Administrator Wednesday, 16 March 2011 08:52 Last Updated Monday, 21 March 2011 07:32 In this paper, we used the Scale Invariant Feature Transform (SIFT) feature for image retrieval. SIFT descriptors are invariant to image scaling, transformation,rotation and partially invariant to illumination changes and affine, gives the loc...

We have developed a software library, LibFeature, that greatly simplifies the task of extracting feature vectors from raw data. The instructions for computing feature vectors from the input data are written in a high-level language, which can be interpreted in real-time, but because the language is deterministic, it can be executed on many feature vectors in parallel, resulting in performance c...

This paper presents an approach on high-level feature detection within video documents, using a Region Thesaurus. A video shot is represented by a single keyframe and MPEG-7 features are extracted locally, from coarse segmented regions. Then a clustering algorithm is applied on those extracted regions and a region thesaurus is constructed to facilitate the description of each keyframe at a high...

Nowadays, more and more images are available. However, to find a required image for an ordinary user is a challenging task. Large amount of researches on image retrieval have been carried out in the past two decades. Traditionally, research in this area focuses on content based image retrieval. However, recent research shows that there is a semantic gap between content based image retrieval and...

This paper presents a method that is capable of robustly estimating gait phase of a human walking using the motion of a sparse cloud of feature points extracted using a standard feature tracker. We first learn statistical motion models of the trajectories we would expect to observe for each of the main limbs. By comparing the motion of the tracked features to our models and integrating over all...

In the digital world, there is a rapid increase in data that is being generated everyday. Obviously, the image data growth is also more. So from a large database containing images it is really hard to mine retrieve images that are relevant for the query. Image Retrieval is a significant research area in the domain of image processing. It contains features for extraction such as shape, texture, ...

We present a new fully automatic algorithm for MR image segmentation. The MR image data is first interpolated for an adequate local feature vector on each voxel. Then, a two-level segmentation scheme is applied. One is a data-oriented low level segmentation, which is based on a modified self-adaptive on-line vector quantization technique. The other is a goal-directed high level processing, whic...