This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an e#cient segmentation algorithm based on this predicate, and show that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties. We apply the algorithm to image segmentation using two different kinds of local neighborhoods in constructing the graph, and illustrate the results with both real and synthetic images. The algorithm runs in time nearly linear in the number of graph edges and is also fast in practice. An important characteristic of the method is its ability to preserve detail in low-variability image regions while ignoring detail in high-variability regions.

This paper presents a new method for constructing models from a set of positive and negative sample images; the method requires no manual extraction of significant objects or features. Our model representation is based on two layers. The first one consists of “generic ” descriptors which represent sets of similar rotational invariant feature vectors. Rotation invariance allows to group similar, but rotated patterns and makes the method robust to model deformations. The second layer is the joint probability on the frequencies of the “generic ” descriptors over neighborhoods. This probability is multi-modal and is represented by a set of “spatial-frequency ” clusters. It adds a statistical spatial constraint which is rotationally invariant. Our twolayer representation is novel; it allows to efficiently capture “texture-like ” visual structure. The selection of distinctive structure determines characteristic model features (common to the positive and rare in the negative examples) and increases the performance of the model. Models are retrieved and localized using a probabilistic score. Experimental results for “textured ” animals and faces show a very good performance for retrieval as well as localization. 1.

Evaluation of retrieval performance is a crucial problem in content-based image retrieval (CBIR). Many different methods for measuring the performance of a system have been created and used by researchers. This article discusses the advantages and shortcomings of the performance measures currently used. Problems such as dening a common image database for performance comparisons and a means of getting relevance judgments (or ground truth) for queries are explained. The relationship between CBIR and information retrieval (IR) is made clear, since IR researchers have decades of experience with the evaluation problem. Many of their solutions can be used for CBIR, despite the dierences between the fields. Several methods used in text retrieval are explained. Proposals for performance measures and means of developing a standard test suite for CBIR, similar to that used in IR at the annual Text REtrieval Conference (TREC), are presented.

This paper addresses the problem of segmenting an image into regions. We develop a framework for image segmentation based on the intuition that there should be evidence for a boundary between each pair of neighboring regions. This framework provides precise definitions of what it means for a segmentation to be too coarse or too fine, in terms of boundaries between pairs of regions. Within this framework, we de ne a pairwise region comparison function using standard graph-based representations of an image. Then we present an efficient algorithm for computing a segmentation based on this comparison function, and prove that it produces segmentations that satisfy the global properties of being neither too coarse nor too fine according to the framework. We apply this algorithm to image segmentation using two different graph-based representations of an image, and illustrate the results with both real and synthetic images. The algorithm runs in time nearly linear in the number of graph edges and is also fast in practice. An important characteristic of the method is its ability to preserve detail in low-variability image regions while ignoring detail in high-variability regions.

This paper proposes structural features for content-based image retrieval (CBIR), especially edge/structure features extracted from edge maps. The feature vector is computed through a “Water-Filling Algorithm ” applied on the edge map of the original image. The purpose of this algorithm is to efficiently extract information embedded in the edges. The new features are more generally applicable than texture or shape features. Experiments show that the new features can catch salient edge/structure information and improve the retrieval performance.

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The performance of a content-based image retrieval (CBIR) system is inherently constrained by the features adopted to represent the images in the database. Use of low-level features can not give satisfactory retrieval results in many cases; especially when the high-level concepts in the user’s mind is not easily expressible in terms of the low-level features. Therefore whenever possible, textual annotations shall be added or extracted and/or processed to improve the retrieval performance. In this paper a hybrid image retrieval system is presented to provide the user with the flexibility of using both the high-level semantic concept/keywords as well as low-level feature content in the retrieval process. The emphasis is put on a statistical algorithm for semantic grouping in the concept space through relevance feedback in the image space. Under this framework, the system can also incrementally learn the user’s search habit/preference in terms of semantic relations among concepts; and uses this information to improve the performance of subsequent retrieval tasks. This algorithm can eliminate the need for a stand-alone thesaurus, which may be too large in size and contain too much redundant information to be of practical use. Simulated experiments are designed to test the effectiveness of the algorithm. An intelligent dialog system, to which this algorithm can be a part of the knowledge acquisition module, is also described as a front end for the CBIR system. Keywords: Content-based image retrieval; semantic grouping; automatic thesaurus generation; information retrieval. 1.

methods, mpeg-7

Humans beings can easily make abstract judgments of similarity, but current techniques for algorithmically measuring the similarity between two images do so at a very concrete level, measuring simple statistics computed from the rawimage pixels. This dissertation develops and evaluates an evolvable framework for computing image similarity that moves toward more abstract forms of similarity, particularly by allowing the comparison of images based only upon certain significant portions. We begin by formulating and stating the area-matching assumption for concrete visual similarity: Two images are likely to be similar to the extent that they comprise equally matched areas of visually similar materials. We develop an infrastructure to test and explore this approach, and extend it to applications such as classification, image retrieval, and object retrieval. The infrastructure extends from early phases of image processing and analysis, through to multiple-image comparisons and frameworks for applying sophisticated learning algorithms. Throughout we apply the best available tests to evaluate the new techniques and compare them to existing methods. We begin with basic image processing tools that contribute to successful image comparisons. A multi-tiered model-based segmentation algorithm identifies regions of uniform visual

Abstract—We propose a method for concept-based medical image retrieval that is a superset of existing semantic-based image retrieval methods. We conceive of a concept as an incremental and interactive formalization of the user’s conception of an object in an image. The premise is that such a concept is closely related to a user’s specific preferences and subjectivity and, thus, allows to deal with the complexity and content-dependency of medical image content. We describe an object in terms of multiple continuous boundary features and represent an object concept by the stochastic characteristics of an object population. A population-based incrementally learning technique, in combination with relevance feedback, is then used for concept customization. The user determines the speed and direction of concept customization using a single parameter that defines the degree of exploration and exploitation of the search space. Images are retrieved from a database in a limited number of steps based upon the customized concept. To demonstrate our method we have performed concept-based image retrieval on a database of 292 digitized X-ray images of cervical vertebrae with a variety of abnormalities. The results show that our method produces precise and accurate results when doing a direct search. In an open-ended search our method efficiently and effectively explores the search space. Index Terms—Content-based image retrieval, multifeature object description, population-based incremental learning, relevance feedback, visual concept learning. I.