15434765
A Dynamic Sub-vector Weighting Scheme for Image Retrieval with Relevance Feedback
In image retrieval with relevance feedback, feature components obtained from low-level descriptors are often weighted to reflect the high-level concepts and a user’s subjective perception embodied in the images labelled by the user in the feedback. However, the number of the labelled images is often small and an optimal weighting cannot be achieved in practice because of the singularity of the covariance matrix needed for weighting. To solve this problem, a dynamic sub-vector weighting scheme is proposed in this paper. In this scheme, a multi-dimensional feature vector is partitioned into multiple sub-vectors whose dimensions are dynamically adjusted to match the small number of available labelled images. Because of the lower dimensionality of the sub-vectors, the optimal weighting can be performed based on these sub-vectors, respectively. The multiple similarity scores obtained from sub-vector comparisons are then combined, as the final score, to rank the database images and determine the retrieval result. Experimental results demonstrated better performance of the proposed weighting scheme than some existing weighting schemes.
2447873
Multilabel SVM active learning for image classification
Image classification is an important task in computer vision. However, how to assign suitable labels to images is a subjective matter, especially when some images can be categorized into multiple classes simultaneously. Multilabel image classification focuses on the problem that each image can have one or multiple labels. It is known that manually labelling images is time-consuming and expensive. In order to reduce the human effort of labelling images, especially multilabel images, we proposed a multilabel SVM active learning method. We also proposed two selection strategies: Max Loss strategy and Mean Max Loss strategy. Experimental results on both artificial data and real-world images demonstrated the advantage of proposed method.
7983053
Auto-Calibration of a Compound-Type Omnidirectional Camera
We propose a method for auto-calibrating a compound-type omnidirectional camera (system). This camera produces spherical panoramic image through stitching some individual images captured by several modular cameras, each of which covers a different field of view of the full viewing sphere. By auto-calibration we mean that our algorithm works online in a self-calibration manner: it receives as input image point correspondences only, and outputs all the camera parameters. The calibration task consists of lens-distortion-removal, intrinsic-calibration, and relative-orientation. The focus of the paper is an algorithm for automatically removing lens distortion. By exploiting planar projective invariants, our algorithm is capable of estimating centre-of-distortion and distortion parameters efficiently without using any nonlinear iteration.
16763535
Handling Significant Scale Difference for Object Retrieval in a Supermarket
We propose an object retrieval application which can retrieve user specified objects from a big supermarket. Significant and unpredictable scale difference between the query and the database image is the major obstacle encountered. The widely used local invariant features show their deficiency in such an occasion. To improve the situation, we first design a new weighting scheme which can assess the repeatability of local features against scale variance. Also, another method which deals with scale difference through retrieving a query under multiple scales is also developed. Our methods have been tested on a real image database collected from a local supermarket and outperform the existing local invariant feature based image retrieval approaches. A new spatial check method is also briefly discussed.
17672081
A Sub-Vector Weighting Scheme for Image Retrieval with Relevance Feedback
In image retrieval with relevance feedback, feature components are weighted to reflect the high-level concepts, and a user's subjective perception, embodied in the images labelled by the user in the feedback. However, the number of labelled images is often small and the covariance matrix needed for weighting will be singular. For this reason, the commonly used methods discard the mutual correlation among the feature components completely and use a diagonal covariance matrix. In this paper, a sub-vector weighting scheme is proposed. This scheme partitions a multi-dimensional visual feature vector into multiple low-dimensional sub-vectors. The singularity of the covariance matrix for each sub-vector can be avoided due to the lower dimensionality of the sub-vectors. Thus, the mutual correlation in each sub-vector can be retained for weighting and an optimally weighted similarity metric can be applied on each sub-vector. The similarity scores obtained from different sub-vectors are combined, as the final scor...
34620301
Video Local Pattern based Image Matching for Visual Mapping
Image matching plays an important role in visual mapping, a critical task of vision based mobile robot navigation. Based on the observation that the visual content in these video sequences generally changes in a slow and continuous mode, a concept of "video local pattern" is proposed to model each video frame, which is defined as a set of frames that are visually-similar and temporally-adjacent to that frame. Instead of manually labelling, a tracking based method is developed to automatically detect the local pattern for each frame. A model is then estimated from a local pattern, and the matching of images is performed by comparing these models. Experimental results demonstrated its improvement over that comparing individual frames
45070598
An Adaptive Spatial Error Concealment for H.264/AVC Video Stream
A drill guide for positioning on a surface of a bone includes a projection for penetrating the bone, an abutment for engaging the surface of the bone to ensure penetration of the bone by the projection to a predetermined depth, less than the length of the projection, and a pair of guide passages having central longitudinal axes, respectively, converging at a predetermined point in the bone when the projection penetrates the bone to the predetermined depth. A kit for fixating soft tissue to bone includes a drill guide, a drill bit, a suture and a suture passer. The drill bit is insertable in a pair of guide passages of the drill guide to cut first and second passages converging in the bone to define a continuous tunnel. The suture passer has a portion for being introduced in the tunnel to engage the suture externally of the bone such that the suture is drawn through the tunnel when the suture passer is withdrawn therefrom. A method of fixating soft tissue to bone includes the steps of penetrating a bone with a projection of a drill guide, engaging the bone with an abutment of the drill guide to ensure penetration of the bone by the projection to a predetermined depth, introducing a drill bit through a pair of guide passages of the drill guide and into the bone to form first and second passages converging in the bone to define a continuous tunnel, introducing a suture passer into the tunnel such that a suture engaging member of the suture passer protrudes externally of the bone, engaging a suture with the suture engaging member, withdrawing the suture passer from the tunnel to draw the suture through the tunnel and using the suture to fixate soft tissue to the bone.
20762776
A Symmetric Orthogonal FastICA Algorithm and Applications in EEG
Extracting a number of independent components,FastICA algorithm can ensure that, each component extracted has never been extracted by adding orthogonalization steps. However, Gram-Schmidt orthogonal method implemented itself that estimation error of the first vector has been accumulated in the subsequent vectors. In this paper, the square root of the classical matrix method is used to achieve symmetric orthogonal that parallel to estimate the source of variables and avoid the Gram-Schmidt orthogonal method of estimation error accumulation problem. The symmetric orthogonal FastICA algorithm is apply in removal EEG artifacts (including eye movement, blinking, ECG, EMG, etc). The experiment and simulation results demonstrate that the symmetry orthogonal FastICA algorithm removes the artifacts and has a better separation efficiency and fast convergence.
9818080
Automatic image annotation and retrieval using weighted feature selection
The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, image data usually have a large number of dimensions. Traditional clustering algorithms assign equal weights to these dimensions, and become confounded in the process of dealing with these dimensions. In this paper, we propose weighted feature selection algorithm as a solution to this problem. For a given cluster, we determine relevant features based on histogram analysis and assign greater weight to relevant features as compared to less relevant features. We have implemented various different models to link visual tokens with keywords based on the clustering results of K-means algorithm with weighted feature selection and without feature selection, and evaluated performance using precision, recall and correspondence accuracy using benchmark dataset. The results show that weighted feature selection is better than traditional ones for automatic image annotation and retrieval.
14052879
Automatic image annotation and retrieval using subspace clustering algorithm
The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, image data usually have a large number of dimensions. Traditional clustering algorithms assign equal weights to these dimensions, and become confounded in the process of dealing with these dimensions. In this paper, we propose a top-down, subspace clustering algorithm as a solution to this problem. For a given cluster, we determine relevant features based on histogram analysis and assign greater weight to relevant features as compared to less relevant features. We have implemented four different models to link visual tokens with keywords based on the clustering results of our clustering algorithm and K-means algorithm, and evaluated performance using precision, recall and correspondence accuracy using benchmark dataset. The results show that our algorithm is better than traditional ones for automatic image annotation and retrieval.
28547446
Low-Complex Equalization for Single Carrier Systems Employing Cyclic Prefix over Time-Variant Channels
A simplified equalization method based on the band structure of the frequency domain channel matrix is proposed for the single carrier systems employing cyclic prefix (SC-CP) over time-varying wireless channels. Using both theoretical analysis and computer simulation, it is shown that the complexity of this method is proportional to the number of symbols in one SC-CP block and is less than that of traditional block equalization methods. We also show that they have similar performance.