Target search in content-based image retrieval (CBIR) systems refers to finding a specific (target) image such as a particular registered logo or a specific historical photograph. Existing techniques, designed around query refinement based on relevance feedback, suffer from slow convergence, and do not guarantee to find intended targets. To address these limitations, we propose several efficient query point movement methods. We prove that our approach is able to reach any given target image with fewer iterations in the worst and average cases. We propose a new index structure and query processing technique to improve retrieval effectiveness and efficiency. We also consider strategies to minimize the effects of users' inaccurate relevance feedback. Extensive experiments in simulated and realistic environments show that our approach significantly reduces the number of required iterations and improves overall retrieval performance. The experimental results also confirm that our approach can always retrieve intended targets even with poor selection of initial query points.

Data clustering is a difficult problem due to the complex and heterogeneous natures of multidimensional data. To improve clustering accuracy, we propose a scheme to capture the local correlation structures: associate each cluster with an independent weighting vector and embed it in the subspace spanned by an adaptive combination of the dimensions. Our clustering algorithm takes advantage of the known pairwise instance-level constraints. The data points in the constraint set are divided into groups through inference; and each group is assigned to the feasible cluster which minimizes the sum of squared distances between all the points in the group and the corresponding centroid. Our theoretical analysis shows that the probability of points being assigned to the correct clusters is much higher by the new algorithm, compared to the conventional methods. This is confirmed by our experimental results, indicating that our design indeed produces clusters which are closer to the ground truth than clusters created by the current state-of-the-art algorithms.

We examine the problem of efficient distance-based similarity search over high-dimensional data. We show that a promising approach to this problem is to reduce dimensions and allow fast approximation. Conventional reduction approaches, however, entail a significant shortcoming: The approximation volume extends across the dataspace, which causes overestimation of retrieval sets and impairs performance. This paper focuses on a new criterion for dimensionality reduction methods: bounded approximation. We show that this requirement can be accomplished by a novel nonlinear transformation scheme that extracts two important parameters from the data. We devise two approximation formulations, namely, rectangular and spherical range search, each corresponding to a closed volume around the original search sphere. We discuss in detail how we can derive tight bounds for the parameters and prove further results, as well as highlight insights into the problems and our proposed solutions. To demonstrate the benefits of the new criterion, we study the effects of (un)boundedness on approximation performance, including selectivity, error toleration, and efficiency. Extensive experiments confirm the superiority of this technique over recent state-of-the-art schemes.

In this paper, we propose Local and Global Structures Preserving Projection (LGSPP), which is to find a small set of projection directions so as to properly preserve the local and global structures for a given set of data. Specifically, for each point in the dataset, its local neighborhood is extracted as well as a set of sampled points far away from this point, which characterize the global structure. The embedding minimizes the distances of the points in each local neighborhood while dispersing them far apart from their corresponding remote points. In this way, the local-global relationships between data points are well kept.

In a typical image retrieval system, all visual features of query images are used to determine image similarity. Thus, users are left to decide whether or not to include images that not only contain desirable features but also irrelevant ones. Fewer examples or a contaminated set of more could compromise the retrieval effectiveness of most similarity measures. In this paper, we extend our previous approach that allows users define queries by specifying relevant features present in image examples. The extended technique support queries decomposed in multiple clusters, each forming a subquery. Our experimental results have shown a remarkable improvement in retrieval performance

Many current image retrieval techniques allow queries to be defined with multiple examples from a presented set. In these systems, all visual features are extracted from these images and used to determine relevant images from the database. As a result, users are left to decide whether or not to include images that not only contain desirable features but also irrelevant ones. Fewer examples or a contaminated set of more either would compromise the retrieval effectiveness of most similarity measures. In this work, we examine this popular case when desired features present in image examples define the intent of the queries. We show how this consideration affects the selection of the representative query points and retrieval sets, and discuss the options whether or not to retrieve partially relevant images. Our experimental results have shown a remarkable improvement in retrieval performance.

In this paper, we argue that two inferences can be made from the results of recent studies: the continuity of image representation and the non-homogeneity of feature space. These characteristics enable the identification of points that satisfy the constraints established in multipoint queries in any orthogonal feature representations. The set can be described by a linear system of equations with constraints. We look at several existing techniques and propose one that can retrieve the set efficiently. We evaluated the performance of our present technique on large sets of images. The results indicate that the new representation matches semantic classes of images very well. The superiority of our approach is evident in the experimental results and in actual implementation.

To enable efficient similarity search in large databases, many indexing techniques use a linear transformation scheme to reduce dimensions and allow fast approximation. In this reduction approach the approximation is unbounded, so that the approximation volume extends across the dataspace. This causes over-estimation of retrieval sets and impairs performance.This paper presents a non-linear transformation scheme that extracts two important parameters specifying the data. We prove that these parameters correspond to a bounded volume around the search sphere, irrespective of dimensionality. We use a special workspace-mapping mechanism to derive tight bounds for the parameters and to prove further results, as well as highlighting insights into the problems and our proposed solutions. We formulate a measure that lower-bounds the Euclidean distance, and discuss the implementation of the technique upon a popular index structure. Extensive experiments confirm the superiority of this technique over recent state-of-the-art schemes.

Image enhancement such as adjusting brightness and contrast is central to improving human visualization of images’ content. Images in desired enhanced quality facilitate analysis, interpretation, classification, information exchange, indexing and retrieval. The adjustment process, guided by diverse enhancement objectives and subjective human judgment, often produces various versions of the same image. Despite the preservation of content under these operations, enhanced images are treated as new in most existing techniques via their widely different features. This leads to difficulties in recognition and retrieval of images across application domains and user interest. To allow unrestricted enhancement flexibility, accurate identification of images and their enhanced versions is therefore essential. In this paper, we introduce a measure that theoretically guarantees the identification of all enhanced images originated from one. In our approach, images are represented by points in multidimensional intensity-based space. We show that points representing images of the same content are confined in a well-defined area that can be identified by a so-devised formula. We evaluated our technique on large sets of images from various categories, including medical, satellite, texture, color images and scanned documents. The proposed measure yields an actual recognition rate approaching 100% in all image categories, outperforming other well-known techniques by a wide margin. Our analysis at the same time can serve as a basis for determining the minimum criterion a similarity measure should satisfy. We discuss also how to apply the formula as a similarity measure in existing systems to support general image retrieval.

We propose a novel, shape-matching algorithm using skeletal graphs. The topology of skeletal graphs is captured and compared at the node level. Such graph representation allows preservation of the skeletal graph's coherence without scarifying the flexibility of matching similar portions of graphs across different levels. By using an appropriate sampling resolution, we are able to achieve a high recognition rate, and at the same time, significantly reduce the space and time complexity of matching. We tested our approach against the directed acyclic graph (DAG) method on noisy graphs and occluded or cluttered scenes. The results show that our approach is an effective and efficient technique for shape recognition.

Segmentation of ultrasound liver images presents a unique challenge because these images contain strong speckle noise and attenuated artifacts. Most ultrasound image segmentation techniques focus on region growing or active contours. These are semi-automatic segmenting systems, in which seed points or initial contours have to be manually identified. In this paper, we propose a fully automatic segmentation system for ultrasound liver images. We apply the Peak-and-valley method to pixels scanned along the Hubert curve, and propose a "windows adaptive threshold" procedure to further reduce noise from the images. After Otsu's segmentation algorithm is applied to the images, a core area algorithm is employed to detect liver objects with the help of a feature knowledge base. We compared our method with other techniques and the manual segmentation method. The results indicate the accuracy of our system and our automatically segmented images contain less noise than the other methods.

Query-by-example is the most popular query model in recent content-based image retrieval (CBIR) systems. A typical query image includes relevant objects (e.g., Eiffel Tower), but also irrelevant image areas (including background). The irrelevant areas limit the effectiveness of existing CBIR systems. To overcome this limitation, the system must be able to determine similarity based on relevant regions alone. We call this class of queries region-of-interest (ROI) queries and propose a technique for processing them in a sampling-based matching framework. A new similarity model is presented and an indexing technique for this new environment is proposed. Our experimental results confirm that traditional approaches, such as Local Color Histogram and Correlogram, suffer from the involvement of irrelevant regions. Our method can handle ROI queries and provide significantly better performance. We also assessed the performance of the proposed indexing technique. The results clearly show that our retrieval procedure is effective for large image data sets.

In this paper, we propose a novel segmenting system for ultrasound images. This solution is separated into three steps. First, we filter noise by using the "peak-and-valley" with scanning pixels along the Hilbert curve. Then we use the "Cubic Spline Interpolation" between local peaks and valleys to smooth the image. Second, we present windows adaptive threshold, to eliminate trial and error, as the method for obtaining the right threshold for beginning segmentation. Third, we label distinct, disconnected objects and use our "core area" to detect the object of interest based on the feature knowledge bases. Our method was experimented with liver ultrasound images. We compared the orientation and centroid feature vectors of our Full Automatic Segmenting Ultrasound (FASU) method with the manual segmentation method. The results are fully automatic and confirm the accuracy of our FASU method.

A typical query image contains not only relevant objects, but also irrelevant image areas. The latter, referred to as noise, has limited the effectiveness of existing image retrieval systems. In this paper, we propose a technique that allows users to define arbitrary-shaped queries out of example images. We present a new similarity model, and introduce an indexing technique for this new environment. Our query model is more expressive than the standard query-by-example. The user can draw a contour around a number of objects to specify spatial (relative distance) and scaling (relative size) constraints among them, or use separate contours to disassociate these objects. Our experimental results confirm that traditional approaches, such as Local Color Histogram and Correlogram, suffer from noisy queries. In contrast, our method can leverage arbitrary-shaped queries to offer significantly better performance. This is achieved using only a fraction of the storage overhead required by the other two techniques.

RAID (redundant array of inexpensive disks) offers high performance for read accesses and large writes to many consecutive blocks. On small writes, however, it entails large penalties. Two approaches have been proposed to address this problem. The first approach records the update information on a separate log disk, and only brings the affected parity blocks to the consistent state when the system is idle. This strategy increases the chance of disk failure due to the additional log disks. Furthermore, heavy system loads for an extended period of time can overflow the log disks and cause sudden disastrous performance. The second approach avoids the above problems by grouping the updated blocks into new stripes and writing them as large writes. Unfortunately, this strategy improves write performance on the expense of read operations. After many updates, a set of logically consecutive data blocks can migrate to only a few disks making fetching them more expensive. We improve on the second approach by eliminating its negative side effects. Our simulation results indicate that the existing scheme sometime performs worse than the standard RAIDS design. Our method is consistently better than either of these techniques.

The rapid growth of digital image data increases the need for efficient and effective image retrieval systems. Such systems should provide functionality that tailors to the user's need at the query time. In this paper, we propose a new image retrieval technique that allows users to control the relevantness of the results. For each image, the color contents of its regions are captured and used to compute similarity. Various factors, assigned automatically or by the user, allow high recall and precision to be obtained. We implemented the proposed technique for a large database of 16,000 images. Our experimental results show that this technique is not only space-time efficient but also more effective than recently proposed color histogram techniques.

Periodic broadcast and scheduled multicast have been shown to be very effective in reducing the demand on server bandwidth. While periodic broadcast is ideally suited for very popular videos, scheduled multicast is better for less demanded objects. Work has also been done to show that a hybrid of these techniques offers the best performance. Existing hybrid techniques, however, assume that the workload does not change with time. This assumption is not true for many applications, such as movie on demand, digital video libraries, or electronic commerce. In this paper we show evidence that existing scheduled multicast techniques are not suited for hybrid designs. To address this issue, we propose a new scheme, and use it to design an adaptive hybrid strategy which adjusts itself to cope with a changing workload. We provide simulation results to show that the proposed technique is significantly better than the best static approach in terms of service latency throughput, defection rate, and unfairness.