Benchmarking for Content-Based Visual Information Search

Leung, Clement H. C.; Ip, Horace H. S.

doi:10.1007/3-540-40053-2_39

Cited by 32 publications

(24 citation statements)

References 17 publications

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“…To evaluate the effectiveness of the proposed framework, experiments were performed on a generalpurpose image database with 3000 images collected from COREL and IAPR image collection [13]. This database contains diverse images of 15 manually assigned semantical categories (Mountain, Beach, Archi- For a quantitative evaluation, the performances of individual and fusion-based similarity measures are compared based on average precision curves by evaluating top 200 returned results.…”

Section: Resultsmentioning

confidence: 99%

A Feature Level Fusion in Similarity Matching to Content-Based Image Retrieval

Rahman

Desai

Bhattacharya

2006

2006 9th International Conference on Information Fusion

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Section: Resultsmentioning

confidence: 99%

A Feature Level Fusion in Similarity Matching to Content-Based Image Retrieval

Rahman

Desai

Bhattacharya

2006

2006 9th International Conference on Information Fusion

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“…The most interesting of these, and in some sense the closest to our own work, are the papers by Muller et al 5 and Leung and Ip. 6 Unlike their proposals, however, our paper presents an implementation of a CBIR benchmark together with its attendant methodology. As far as we are aware, our benchmark methodology differs significantly from anything that has been proposed previously.…”

Section: Introductionmentioning

confidence: 91%

“…The advent of Webbased image retrieval sytems [20][21][22] suggests this focus on algorithmic performance has become too narrow. 5,6 Our philosophy is captured in the choice of name for the first implementation of such a benchmark viz., BIRDS-I an acronym for Benchmark for Image Retrieval using Distributed Systems over the Internet. This name is intended to invoke the dual notions of: 1.…”

Section: Systems Perspectivementioning

confidence: 99%

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<title>Benchmark for image retrieval using distributed systems over the Iinternet: BIRDS-I</title>

Günther

Beretta

2000

SPIE Proceedings

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Comparing the performance of CBIR (Content-Based Image Retrieval) algorithms is difficult. Private data sets are used so it is controversial to compare CBIR algorithms developed by different researchers. Also, the performance of CBIR algorithms is usually measured on an isolated, well-tuned PC or workstation. In a real-world environment, however, the CBIR algorithms would only constitute a minor component among the many interacting components needed to facilitate a useful CBIR application e.g., Web-based applications on the Internet. The Internet, being a shared medium, dramatically changes many of the usual assumptions about measuring CBIR performance. Any CBIR benchmark should be designed from a networked systems standpoint. Networked system benchmarks have been developed for other applications e.g., text retrieval, and relational database management. These benchmarks typically introduce communication overhead because the real systems they model are distributed applications e.g., an airline reservation system. The most common type of distributed computing architecture uses a client/server model. We present our implementation of a client/server CBIR benchmark called BIRDS-I (Benchmark for Image Retrieval using Distributed Systems over the Internet) to measure image retrieval performance over the Internet. The BIRDS-I benchmark has been designed with the trend toward the use of small personalized wireless-internet systems in mind. Web-based CBIR implies the use of heterogeneous image sets and this, in turn, imposes certain constraints on how the images are organized and the type of performance metrics that are applicable. Surprisingly, BIRDS-I only requires controlled human intervention for the compilation of the image collection and none for the generation of ground truth in the measurement of retrieval accuracy. Benchmark image collections need to be evolved incrementally toward the storage of millions of images and that scaleup can only be achieved through the use of computeraided compilation. Finally, the BIRDS-I scoring metric introduces a tightly optimized image-ranking window, which is important for the future benchmarking of large-scale personalized wireless-internet CBIR systems.

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“…Therefore, it may be justified to use an extensive repertoire of measures, especially when comparing two considerably different systems. Recommendations for selections of benchmark measures are provided by Smith (1998), Leung and Ip (2000), and Müller et al (2001).…”

Section: Evaluation Measuresmentioning

confidence: 99%

Content-Based Image Retrieval Using Self-Organizing Maps

Laaksonen

Koskela

Oja

1999

Visual Information and Information Systems

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Digital image libraries are becoming more common and widely used as visual information is produced at a rapidly growing rate. Creating and storing digital images is nowadays easy and getting more affordable all the time as the needed technologies are maturing and becoming eligible for general use. As a result, the amount of data in visual form is increasing and there is a strong need for effective ways to manage and process it. In many settings, the existing and widely adopted methods for text-based indexing and information retrieval are inadequate for these new purposes.Content-based image retrieval addresses the problem of finding images relevant to the users' information needs from image databases, based principally on low-level visual features for which automatic extraction methods are available. Due to the inherently weak connection between the high-level semantic concepts that humans naturally associate with images and the low-level visual features that the computer is relying upon, the task of developing this kind of systems is very challenging. A popular method to improve retrieval performance is to shift from single-round queries to navigational queries where a single retrieval instance consists of multiple rounds of user-system interaction and query reformulation. This kind of operation is commonly referred to as relevance feedback and can be considered as supervised learning to adjust the subsequent retrieval process by using information gathered from the user's feedback.In this thesis, an image retrieval system named PicSOM is presented, including detailed descriptions of using multiple parallel Self-Organizing Maps (SOMs) for image indexing and a novel relevance feedback technique. The proposed relevance feedback technique is based on spreading the user responses to local SOM neighborhoods by a convolution with a kernel function. A broad set of evaluations with different image features, retrieval tasks, and parameter settings demonstrating the validity of the retrieval method is described. In particular, the results establish that relevance feedback with the proposed method is able to adapt to different retrieval tasks and scenarios.Furthermore, a method for using the relevance assessments of previous retrieval sessions or potentially available keyword annotations as sources of semantic information is presented. With performed experiments, it is confirmed that the efficiency of semantic image retrieval can be substantially increased by using these features in parallel with the standard low-level visual features.

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Benchmarking for Content-Based Visual Information Search

Cited by 32 publications

References 17 publications

A Feature Level Fusion in Similarity Matching to Content-Based Image Retrieval

A Feature Level Fusion in Similarity Matching to Content-Based Image Retrieval

<title>Benchmark for image retrieval using distributed systems over the Iinternet: BIRDS-I</title>

Content-Based Image Retrieval Using Self-Organizing Maps

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