5498052
Natural image retrieval based on features of homogeneous color regions
Natural image retrieval using low-level visual features is a challenging problem for content-based image retrieval. In this paper, a region-based image retrieval (RBIR) approach is proposed. Each image is represented by several feature vectors extracted from homogeneous color regions within an image, and similar images are retrieved based on these region features. In the experimental image database all images are grouped into 16 categories using a moment feature to speed up the retrieval performance. Color mean, color histogram and moment of regions are used as features. From the experiments, it is found that region-based retrieval returns more relevant images than using features based on the entire image.
15085849
A novel framework for SVM-based image retrieval on large databases
In this paper, a novel framework is proposed to deliver a fast, robust, and generally applicable SVM-based image retrieval for large databases. A quick test scheme is developed, and on-line kernel learning is employed to realize it after analyzing the relationship between them. Then an upper bound on maximum test scope is derived to speed up testing further. Also, the general applicability is well maintained because this framework does not need a kernel function and index structure to be pre-defined. Taking the advantages of this framework, more sophisticated SVM can be used to improve retrieval performance while keeping short response time. Experimental results on large image databases verify the effectiveness and efficiency of the proposed framework.
31213439
Nasopharyngeal carcinoma lesion segmentation from MR images by support vector machine
A two-class support vector machine (SVM)-based image segmentation approach has been developed for the extraction of nasopharyngeal carcinoma (NPC) lesion from magnetic resonance (MR) images. By exploring two-class SVM, the developed method can learn the actual distribution of image data without prior knowledge and draw an optimal hyperplane for class separation, via an SVM parameters training procedure and an implicit kernel mapping. After learning, segmentation task is performed by the trained SVM classifier. The proposed technique is evaluated by 39 MR images with NPC and the results suggest that the proposed query-based approach provides an effective method for NPC extraction from MR images with high accuracy
6480622
Mapping low-level features to high-level semantic concepts in region-based image retrieval
In this paper, a novel offline supervised learning method is proposed to map low-level visual features to high-level semantic concepts for region-based image retrieval. The contributions of this paper lie in three folds. (1) For each semantic concept, a set of low-level tokens are extracted from the segmented regions of training images. Those tokens capture the representative information for describing the semantic meaning of that concept; (2) a set of posteriors are generated based on the low-level tokens through pairwise classification, which denote the probabilities of images belonging to the semantic concepts. The posteriors are treated as high-level features that connect images with high-level semantic concepts. Long-term relevance feedback learning is incorporated to provide the supervisory information needed in the above offline learning process, including the concept information and the relevant training set for each concept; (3) an integrated algorithm is implemented to combine two kinds of information for retrieval: the information from the offline feature-to-concept mapping process and the high-level semantic information from the long-term learned memory. Experimental evaluation on 10,000 images proves the effectiveness of our method.
6584143
Learning Similarity for Texture Image Retrieval
A novel algorithm is proposed to learn pattern similarities for texture image retrieval. Similar patterns in different texture classes are grouped into a cluster in the feature space. Each cluster is isolated from others by an enclosed boundary, which is represented by several support vectors and their weights obtained from a statistical learning algorithm called support vector machine (SVM). The signed distance of a pattern to the boundary is used to measure its similarity. Furthermore, the patterns of different classes within each cluster are separated by several sub-boundaries, which are also learned by the SVMs. The signed distances of the similar patterns to a particular sub-boundary associated with the query image are used for ranking these patterns. Experimental results on the Brodatz texture database indicate that the new method performs significantly better than the traditional Euclidean distance based approach.
62767012
Incorporating prior knowledge into SVM for image retrieval
SVM based image retrieval suffers from the scarcity of labelled samples. In this paper, this problem is solved by incorporating prior knowledge into SVM. Firstly, some prior knowledge of image retrieval is discussed and constructed. After that, the knowledge is incorporated into SVM optimization as a constraint, and a new knowledge-based target function is formulated. Based on this, a framework of image retrieval with knowledge based SVM is proposed. Experimental results demonstrate that the proposed method can effectively improve the learning and retrieval performance of SVM, especially when the number of labelled samples is small.
5317364
A criterion for optimizing kernel parameters in KBDA for image retrieval
A criterion is proposed to optimize the kernel parameters in Kernel-based Biased Discriminant Analysis (KBDA) for image retrieval. Kernel parameter optimization is performed by optimizing the kernel space such that the positive images are well clustered while the negative ones are pushed far away from the positives. The proposed criterion measures the goodness of a kernel space, and the optimal kernel parameter set is obtained by maximizing this criterion. Retrieval experiments on two benchmark image databases demonstrate the effectiveness of proposed criterion for KBDA to achieve the best possible performance at the cost of a small fractional computational overhead.
46281578
Model-Based Active Object Recognition Using MRF Matching and Sensor Planning
This paper presents an active object recognition algorithm using MAP-MRF matching and sensor planning strategy. The matching between the sensed and model object is based on surface properties. A new measure of surface distinguishability is defined for sensor planning. MAP-MRF framework is used for generating matching label set. A measure of confidence of correct match is determined based on the posterior energy. An active object recognition algorithm is used for determining the next viewpoint of the camera if ambiguity exists in the matching result. The next viewpoint is chosen based the surface with highest distinguishability. Experimental results on images under perfect and imperfect segmentation are presented.
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.
18212897
Performance Evaluation of the Nearest Feature Line Method in Image Classification and Retrieval
A method, the nearest feature line (NFL) method, is used in image classification and retrieval and its performance is evaluated and compared with other methods by extensive experiments. The NFL method is demonstrated to make efficient use of knowledge about multiple prototypes of a class to represent that class.
18666864
An evolutionary clustering algorithm for gene expression microarray data analysis
Clustering is concerned with the discovery of interesting groupings of records in a database. Many algorithms have been developed to tackle clustering problems in a variety of application domains. In particular, some of them have been used in bioinformatics research to uncover inherent clusters in gene expression microarray data. In this paper, we show how some popular clustering algorithms have been used for this purpose. Based on experiments using simulated and real data, we also show that the performance of these algorithms can be further improved. For more effective clustering of gene expression microarray data, which is typically characterized by a lot of noise, we propose a novel evolutionary algorithm called evolutionary clustering (EvoCluster). EvoCluster encodes an entire cluster grouping in a chromosome so that each gene in the chromosome encodes one cluster. Based on such encoding scheme, it makes use of a set of reproduction operators to facilitate the exchange of grouping information between chromosomes. The fitness function that the EvoCluster adopts is able to differentiate between how relevant a feature value is in determining a particular cluster grouping. As such, instead of just local pairwise distances, it also takes into consideration how clusters are arranged globally. Unlike many popular clustering algorithms, EvoCluster does not require the number of clusters to be decided in advance. Also, patterns hidden in each cluster can be explicitly revealed and presented for easy interpretation even by casual users. For performance evaluation, we have tested EvoCluster using both simulated and real data. Experimental results show that it can be very effective and robust even in the presence of noise and missing values. Also, when correlating the gene expression microarray data with DNA sequences, we were able to uncover significant biological binding sites (both previously known and unknown) in each cluster discovered by EvoCluster.