Human-Centered Content-Based Image Retrieval

Retrieval of images that lack a (suitable) annotations cannot be achieved through (traditional) Information Retrieval (IR) techniques. Access through such collections can be achieved through the application of computer vision techniques on the IR problem, which is baptized Content-Based Image Retrieval (CBIR). In contrast with most purely technological approaches, the thesis Human-Centered Content-Based Image Retrieval approaches the problem from a human/user centered perspective. Psychophysical experiments were conducted in which people were asked to categorize colors. The data gathered from these experiments was fed to a Fast Exact Euclidean Distance (FEED) transform (Schouten & Van den Broek, 2004), which enabled the segmentation of color space based on human perception (Van den Broek et al., 2008). This unique color space segementation was exploited for texture analysis and image segmentation, and subsequently for full-featured CBIR. In addition, a unique CBIR-benchmark was developed (Van den Broek et al., 2004, 2005). This benchmark was used to explore what and how several parameters (e.g., color and distance measures) of the CBIR process influence retrieval results. In contrast with other research, users judgements were assigned as metric. The online IR and CBIR system Multimedia for Art Retrieval (M4ART) (URL: http://www.m4art.org) has been (partly) founded on the techniques discussed in this thesis. References: - Broek, E.L. van den, Kisters, P.M.F., and Vuurpijl, L.G. (2004). The utilization of human color categorization for content-based image retrieval. Proceedings of SPIE (Human Vision and Electronic Imaging), 5292, 351-362. [see also Chapter 7] - Broek, E.L. van den, Kisters, P.M.F., and Vuurpijl, L.G. (2005). Content-Based Image Retrieval Benchmarking: Utilizing Color Categories and Color Distributions. Journal of Imaging Science and Technology, 49(3), 293-301. [see also Chapter 8] - Broek, E.L. van den, Schouten, Th.E., and Kisters, P.M.F. (2008). Modeling Human Color Categorization. Pattern Recognition Letters, 29(8), 1136-1144. [see also Chapter 5] - Schouten, Th.E. and Broek, E.L. van den (2004). Fast Exact Euclidean Distance (FEED) transformation. In J. Kittler, M. Petrou, and M. Nixon (Eds.), Proceedings of the 17th IEEE International Conference on Pattern Recognition (ICPR 2004), Vol 3, p. 594-597. August 23-26, Cambridge - United Kingdom. [see also Appendix C

Je persoonlijke hybride zoekmachine met menselijke trekjes.

Een nieuwe generatie zoekmachines is in ontwikkeling om de steeds meer eisende consument tevreden te stellen. Tekst- en beeldanalyse worden gecombineerd tot een hybride zoeksysteem. De menselijke cognitie dient als voorbeeld. Naadloze aansluiting op het denken van de individuele gebruikers is het doel. Maar de praktijk blijkt weerbarstig

Human-Centered Object-Based Image Retrieval

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Human-centered object-based image retrieval

Abstract. A new object-based image retrieval (OBIR) scheme is introduced. The images are analyzed using the recently developed, human-based 11 colors quantization scheme and the color correlogram. Their output served as input for the image segmentation algorithm: agglomerative merging, which is extended to color images. From the resulting coarse segments, boundaries are extracted by pixelwise classification, which are smoothed by erosion and dilation operators. The resulting features of the extracted shapes, completed the data for a <color, texture, shape>-vector. Combined with the intersection distance measure, this vector is used for OBIR, as are its components. Although shape matching by itself provides good results, the complete vector outperforms its components, with up to 80 % precision. Hence, a unique, excellently performing, fast, on human perception based, OBIR scheme is achieved.

The development of a human-centered object-based image retrieval engine

The development of a new object-based image retrieval (OBIR) engine is discussed. Its goal was to yield intuitive results for users by using human-based techniques. The engine utilizes a unique and efficient set of 15 features: 11 color categories and 4 texture features, derived from the color correlogram. These features were calculated for the center object of the images, which was determined by agglomerative merging. Subsequently, OBIR was applied, using the color and texture features of the center objects on the images. The final OBIR engine, as well as all intermediate versions, were evaluated in a CBIR benchmark, consisting of the engine, the Corel image database, and an interface module. The texture features proved to be useful in combination with the 11 color categories. In general, the engine proved to be fast and yields intuitive results for users