Panagiotis Androutsos

Query feedback for interactive image retrieval

From a perceptual standpoint, the subjectivity inherent in understanding and interpreting visual content in multimedia indexing and retrieval motivates the need for online interactive learning. Since efficiency and speed are important factors in interactive visual content retrieval, most of the current approaches impose restrictive assumptions on similarity calculation and learning algorithms. Specifically, content-based image retrieval techniques generally assume that perceptually similar images are situated close to each other within a connected region of a given space of visual features. This paper proposes a novel method for interactive image retrieval using query feedback. Query feedback learns the user query as well as the correspondence between high-level user concepts and their low-level machine representation by performing retrievals according to multiple queries supplied by the user during the course of a retrieval session. The results presented in this paper demonstrate that this algorithm provides accurate retrieval results with acceptable interaction speed compared to existing methods.

Retrieval of images from artistic repositories using a decision fusion framework

The large volumes of artistic visual data available to museums, art galleries, and online collections motivate the need for effective means to retrieve :relevant information from such repositories. The paper proposes a decision making framework for content-based retrieval of art images based on a combination of low-level features. Traditionally, the similarity between two images has been calculated as a weighted distance between two feature vectors. This approach, however, may not be mathematically and computationally appropriate, and does not provide enough flexibility in modeling user queries. The paper proposes a framework that generalizes a wide set of previous approaches to similarity calculation, including the weighted distance approach. Image similarities are obtained through a decision making process based on low-level feature distances using fuzzy theory. The analysis and results indicate that the presented aggregation technique provides an effective, general, and flexible tool for similarity calculation based on the combination of individual descriptors and features.

A distributed fault-tolerant MPEG-7 retrieval scheme based on small world theory

A small world search agent employing peer-to-peer (P2P) concepts borrowed from sociology is employed for performing image retrievals in a small world distributed media index. The Small World Indexing Method (SWIM) allows for a highly networked architecture where index information does not exist as a separate entity on a specific server, but rather is stored within the actual media objects themselves. Since each media object is only responsible for a small portion of the overall index, the loss of portions of the overall network (data objects) accounts for only a small degradation in the overall retrieval performance. Building upon previous work, the graceful degradation which is provided by the SWIM system is addressed here for retrievals which are performed using small world user agents on a large set of MPEG-7 described images.

Aggregation of color and shape features for hybrid query generation in content based visual information retrieval

A fuzzy approach for the aggregation of multiple features in content-based image retrieval is outlined. Color, shape and spatial features extracted using both computational and manual segmentation techniques are used for subsequent generation of hybrid queries to a ground truth image database consisting of architectural photographs. Retrieval results for multiple-feature queries are shown in the form of precision recall graphs. The results indicate that the fuzzy approach presented herein can perform at least as well as a weighted mean approach.

Fuzzy aggregation of image features in content-based image retrieval

The obstacle of generating hybrid queries within the context of content-based image retrieval is still very real. In attempts to overcome this, fuzzy aggregation can be used to combine single, simple index queries into larger, more complex ones. The paper outlines the use of a fuzzy aggregation technique for hybrid querying which has the ability to adjust its behavior according to operator-controlled parameters. The resulting aggregator can be viewed as a feature-adaptive overall similarity measure. We limit the scope of the aggregator to queries involving color content, color coverage, and horizontal/vertical trends, and apply it to a media database comprised of Corel images of fixed size. Preliminary results show promise and illustrate that hybrid queries using the fuzzy aggregator are effective in their ability to retrieve relevant images while suppressing erroneous retrievals when compared to simple, single-feature queries. In addition, the results obtained are at a minimum comparable to multiple-feature queries generated using a weighted mean approach, but exhibiting scalability and greater flexibility in parameter adjustment.

Distributed MPEG-7 image indexing using small world user agents

An open peer-to-peer architecture for performing distributed image indexing and retrieval is proposed. The system employs a sociological model of human acquaintance networks (small world theory) and concepts derived from the nature of the World Wide Web. Retrieval is performed using agents, and a node hopping algorithm is employed that exploits node referrals established from descriptor data stored locally by each node. A general framework for this small world image miner (SWIM) is presented along with a realization using MPEG-7 color structure descriptor data for 2400 images. Results related to search agent path length and network node degree are presented.

Fuzzy aggregation of palette colors for hybrid querying of fine art image databases

As outlined by the ISO committee, the problem of hybrid query generation lies outside the scope of the MPEG-7 standard. This problem of creating intelligent image database queries that both correctly reflect the intentions of the user as well as provide good retrieval results can be approached in various ways. This paper proposes a hybrid query generation scheme which employs fuzzy aggregation for including and excluding palette colors within the context of a fine art database containing various paintings and drawings. The aggregator herein exhibits flexibility in its logical behaviour through parameters that can be set by the designer as well permitting the exclusion of specific colors from queries. This translates to richer controls for a user wishing to locate works from a large art image database that have similar, yet complex color palettes. Experimentation on an image database of 464 paintings and drawings illustrate this fact, and a comparison with a weighted mean approach is provided.

Color edge detectors: a subjective analysis

A subjective analysis is performed on various classical, and order statistic-based color edge detectors. Order statistic edge detectors imply that image pixels within a specific region, are treated statistically such that outliers can be rejected from the general trends in the data. A different type of subjective rating system is employed here and the rationale behind its use is explained. The importance of subjective edge detection lies in the development of a color edge detector which can accurately simulate what is seen by humans.

Colour edge detectors: an overview and comparison

There exist many experimental situations in which a subjective rather than an objective test of a specific variable proves to be a much more relevant method of investigation. Because of the intimate relationship between image processing and the human eye, subjective tests are extremely important in the final judgement if an image is passed by the human eye. In this paper, insight into what method of colour edge detection results in edge maps which are in best accordance with what the human eye sees. In particular, this paper presents a comparison of the relative subjectively-based performances of a group of basic order statistic and difference vector operator detectors.

Graceful image retrieval performance degradation using small world distributed indexing

A small world search agent employing peer-to-peer concepts borrowed from sociology is employed for performing image retrievals in a small world distributed media index. The small world indexing method (SWIM) allows for a highly networked architecture where index information does not exist as a separate entity on a specific server, but rather is stored within the actual media objects themselves. Since each media object is only responsible for a small portion of the overall index, the loss of portions of the overall network (data objects) accounts for only a small degradation in the overall retrieval performance. Building upon previous work, the graceful degradation which is provided by the SWIM system is addressed here for retrievals which are performed using small world user agents on a large set of MPEG-7 described images.