Data Mining - Concepts and Techniques.

We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.

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Image retrieval: Ideas, influences, and trends of the new age

We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

A survey of content-based image retrieval with high-level semantics

A review of content-based image retrieval systems in medical applications—clinical benefits and future directions

In content-based image retrieval (CBIR), experimental (trial-and-error) query with relevance feedback is essential for successful retrieval. Unfortunately, the traditional user interfaces are not suitable for trying different combinations of query examples. This is because first, these systems assume query examples are always added incrementally. Second, the query and the result display are done on the same workspace. Once the user removes an image from the query examples, the image may disappear from the user interface. In addition, it is difficult to combine the result of different queries.

In this paper, we propose a new interface for Content-based image retrieval named ImageGrouper. ImageGrouper is a Group-Oriented user interface in that all operations are done by creating groups of images. This approach has several advantages. First, the users can interactively compare different combinations of the query examples by dragging and grouping the images on the workspace (Query-by-Group). Because the query results are displayed on another pane, the user can quickly review the results and modify the query. Combining different queries is also easy. Furthermore, the concept of “Image Groups” is also applied for annotating and organizing many images. The Annotation-by-Groups method relieves the user of tedious task of annotating textual information on a large number of images. This method realizes a hierarchical annotation of the images as well as Bulk Annotation. The Organize-by-Group method lets the users manipulate the image groups as “Photo Albums” to organize the images. Finally, the usability of the system is compared with the traditional user interfaces. By incorporating the lessons from the experiments, the usability of ImageGrouper is further improved.

ImageGrouper: a group-oriented user interface for content-based image retrieval and digital image arrangement

3D MARS is an interactive visualization system for Content-Based Image Retrieval (CBIR). In 3D MARS, the user browses and queries images in an immersive 3D Virtual Reality space of CAVE. The results of the query are displayed in 3D, so that the user can see the result with respect to three different criteria such as color, texture and structure. The user can examine the result from different view angles by flying-through the space with the joystick. Based on the user’s feedback, the system dynamically reorganize its visualization scheme. By giving meanings to each axis, the user can determine which features are important. In addition, the Sphere mode visualization is provided as a powerful analyzing tool for CBIR researchers.

3D MARS: immersive virtual reality for content-based image retrieval

In this paper we address several aspects of the learning problem in content-based image retrieval (CBIR). First, we introduce the linear and kernel-based biased discriminant analysis, or BiasMap, to fit the unique nature of relevance feedback as a small sample biased classification problem. Secondly, a WARF (word association via relevance feedback) formula is presented for learning keyword relations during the process of relevance feedback. We also introduce our new user interface for CBIR, ImageGrouper, which is designed to support more sophisticated user feedbacks and annotations. Finally, we use the D-EM (Discriminant-EM) algorithm as a way of exploiting unlabeled data in CBIR and offer some insights as to when unlabeled data will help.

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Learning in content-based image retrieval

In Content-based Image Retrieval (CBIR) systems, the Query-by-Example (QBE) approach is commonly used. However, because of inevitable "semantic gaps" between visual features and the user's concepts, trial-and-error query is essential for successful retrieval. Unfortunately, traditional user interfaces are not suitable for trying different combinations of query examples. This is because in these systems, query specification and result display are done on the same workspace. Once the user removes an image from the query examples, the image may disappear from the user interface. In addition, it is difficult to combine the result of different queries.In this paper, we propose a new interface for Content-based image retrieval. In our system, the users can interactively compare different combinations of query examples by dragging and grouping images on the workspace (Query-by-Group.) Because the query results are displayed on another pane, the user can quickly review the results. Combining different queries is also easy. Furthermore, the concept of "image groups" is also applied to annotating and organizing a large number of images. Because the gestural operations of our system is similar to file operations of modern window-based operation systems, users can easily learn to use the system.

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Group-based interface for content-based image retrieval

New relevance feedback algorithms have been developed for content-based image retrieval (CBIR) that allow the user to achieve more flexible query. In conjunction with the new user interface, called group-oriented user interface, the user's interest can be expressed with multiple groups of positive and negative image examples. This provides users with greater flexibility as compared with previous systems that consider image query as one or two-class problems. In this paper, we analyze our new algorithm qualitatively and quantitatively. For comparison with previous approaches, the systems are tested on both toy problems and real image retrieval tasks. From the results of our experiments, we suggest when and how our algorithm has advantages over the previous methods.

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Munehiro Nakazato

Papers

ImageGrouper: a group-oriented user interface for content-based image retrieval and digital image arrangement

3D MARS: immersive virtual reality for content-based image retrieval

Learning in content-based image retrieval

Group-based interface for content-based image retrieval

Evaluating group-based relevance feedback for content-based image retrieval