30275744
Robust Active Shape Model Construction and Fitting for Facial Feature Localization
Active Shape Model (ASM) has proved to be a powerful tool for interpreting face images. However, it may fail in the presence of non-Gaussian noise, or outliers. In this paper, we present a framework for both automatic model construction and efficient model fitting with outliers. In model construction, the training face samples are automatically labeled by local image search using Gabor wavelet features. Then robust principle component analysis (RPCA) is applied to capture the statistics of shape variations. In model fitting, an error function is introduced to deal with the outlier problem, which provides a connection to robust M-estimation. Gauss-Newton algorithm is adopted to efficiently optimize the robust energy function. Extensive experiments demonstrate the efficiency and robustness of our approach over previous methods.
8628384
Fingerprint Indexing Based on LAS Registration
Fingerprint indexing is an efficient technique that greatly improves the performance of fingerprint based person authentication systems by reducing the number of comparison. In this paper, we propose an indexing method based on fingerprint registration with a novel feature called local axial symmetry (LAS). The location and direction estimation of reference point are achieved in a straightforward way after the LAS field is achieved. Then the registered orientation field is utilized as a feature vector to perform the following indexing. A new scheme of the experiment is introduced and satisfactory experimental results are achieved on FVC2000 DB2 that the average search space is only 2.34% of all fingers in the condition of equal-sized training set and testing set.
25611508
Regression Nearest Neighbor in Face Recognition
In this paper, we introduce a regression nearest neighbor framework for general classification tasks. To alleviate potential problems caused by nonlinearity, we propose a kernel regression nearest neighbor (KRNN) algorithm and its convex counterpart (CKRNN) as two specific extensions of nearest neighbor algorithm and present a fast and useful kernel selection method correspondingly. Comprehensive analysis and extensive experiments are used to demonstrate the effectiveness of our methods in real face datasets
9616437
Image database retrieval utilizing affinity relationships
Recent research effort in Content-Based Image Retrieval (CBIR) focuses on bridging the gap between low-level features and high-level semantic contents of images as this gap has become the bottleneck of CBIR. In this paper, an effective image database retrieval framework using a new mechanism called the Markov Model Mediator (MMM) is presented to meet this demand by taking into consideration not only the low-level image features, but also the high-level concepts learned from the history of user's access pattern and access frequencies on the images in the database. Also, the proposed framework is efficient in two aspects: 1) Overhead for real-time training is avoided in the image retrieval process because the high-level concepts of images are captured in the off-line training process. 2) Before the exact similarity matching process, Principal Component Analysis (PCA) is applied to reduce the image search space. A training subsystem for this framework is implemented and integrated into our system. The experimental results demonstrate that the MMM mechanism can effectively assist in retrieving more accurate results from image databases.
1297870
An active learning framework for content-based information retrieval
We propose a general active learning framework for content-based information retrieval. We use this framework to guide hidden annotations in order to improve the retrieval performance. For each object in the database, we maintain a list of probabilities, each indicating the probability of this object having one of the attributes. During training, the learning algorithm samples objects in the database and presents them to the annotator to assign attributes. For each sampled object, each probability is set to be one or zero depending on whether or not the corresponding attribute is assigned by the annotator. For objects that have not been annotated, the learning algorithm estimates their probabilities with biased kernel regression. Knowledge gain is then defined to determine, among the objects that have not been annotated, which one the system is the most uncertain. The system then presents it as the next sample to the annotator to which it is assigned attributes. During retrieval, the list of probabilities works as a feature vector for us to calculate the semantic distance between two objects, or between the user query and an object in the database. The overall distance between two objects is determined by a weighted sum of the semantic distance and the low-level feature distance. The algorithm is tested on both synthetic databases and real databases of 3D models. In both cases, the retrieval performance of the system improves rapidly with the number of annotated samples. Furthermore, we show that active learning outperforms learning based on random sampling.
9803776
Semantic propagation from relevance feedbacks
Relevance feedback has been a very useful tool to enhance the performance of content-based information retrieval (CBIR) systems. To fully make use of the precious user feedback provided to a system, we propose an approach named semantic propagation, which reveals the deep semantic relationships among objects in the database, given a set of relevance feedbacks between object pairs. In particular, we present two semantic propagation algorithms that are applicable to CIBR systems with a feature vector space model and a general metric space model, respectively. Experiments on a 3D model retrieval system and a logo image retrieval system are performed to show the effectiveness of the proposed methods.
5718237
Compression and rendering of concentric mosaics with reference block codec (RBC)
Concentric mosaics have the ability to quickly capture a complete 3D view of a realistic environment and to enable a user to wander freely in the environment. However, the data amount of the concentric mosaics is huge. In this paper, we propose an algorithm to compress the concentric mosaic image array through motion compensation and residue coding, which we called reference block codec. A two-level index table is embedded in the compressed bitstream for random access. During the rendering, the entire compressed concentric mosaic scene is not fully expanded at any time. Instead, only the contents necessary to render the current view are decoded in real time. We denote such rendering scheme as just-in-time rendering. Four decoder caches are implemented to speed up the rendering.
603862
Video scene change detection method using unsupervised segmentation and object tracking
In order to manage the growing amount of video information efficiently, a video scene change detection method is necessary. Many advanced video applications such as video on demand (VOD) and digital library also require the scene change detection to organize the video content. In this paper, we present an effective scene change detection method using an unsupervised segmentation algorithm and the technique of object tracking based on the results of the segmentation. Our results have shown that this method can perform not only accurate scene change detection, but also obtain object level information of the video frames, which is very useful for video content indexing and analysis.