Ono, A et al (1996) A flexible content-based image retrieval system with combined scene description keyword. In: Proceedings of IEEE Int. Conf. on Multimedia Computing and Systems, pp. 201-208, 1996.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....is available this seems like a reasonable approach, although the precision of textual context is likely not as high as manual indexing. More importantly, there are many applications such as home photo albums in which there will be minimal if any collateral text to use for automatic indexing. Ono et al. 1996) have attempted to use image recognition techniques to automatically select appropriate descriptive keywords (within a predefined set) for each image. However, they have only tested their system with limited keywords and image models, so its generalizability to a wide range of image models and ....

Ono, A et al (1996) A flexible content-based image retrieval system with combined scene description keyword. In: Proceedings of IEEE Int. Conf. on Multimedia Computing and Systems, pp. 201-208, 1996.

....(Flickner et al. 1995) and text. To retrieve text queried by an image, text information is automatically extracted from an image at some stage in the retrieval process. Various approaches regarding word annotation for images have been investigated (Kurita et al. 1992; Picard Minka, 1995, Ono et al. 1996; Watanabe Nagao, 1998) However, these approaches are very limited in terms of vocabulary or the domain of images. For real data, it is impossible to segment objects in advance, to assume the number of categories, or to avoid the presence of noise which is difficult to erase. In this paper, we ....

Ono, A., Amano, M., Hakaridani, M., Satou, T. and Sakauchi, M. (1996). A flexible contentbased image retrieval system with combined scene description keyword. Proc. IEEE Computer Society, International ConferenceonMultimedia Computing and Systems '96, 201--208.

.... retrievals [11] Several schemes have been proposed to support such retrievals [10, 11, 14, 19, 23, 26, 27] using both relational and object oriented database design [16, 28] Based on the nature and type of retrieval, content based retrievals are further classified into different categories [12, 14, 29]. Prominent among these are: ffl Retrieval by browsing ffl Retrieval by attributes ffl Retrieval by textual descriptions and keywords ffl Retrieval by similarity measures Retrieval by color and texture information Retrieval bydrawing a rough sketch Retrieval by shape similarity ....

Atsushi Ono, Masashi Amano, and Mituhiro Hakaridani. A Flexible Content-Based Image Retrieval System with Combined Scene Description Keyword. In Proceedings of the International Conference on Multimedia Computing and Systems, pages 201--208, Japan, June 1996.

....objects. It also does not distinguish between central elements in the scene and minor parts (such as foreground and background) Additionally, some visual elements are hard to describe using only text [26] Recent work extends the text based image retrieval to be hierarchical and more flexible [27]. More common approaches to image retrieval are the content based image retrieval systems, in which the keys are low level visual features such as color, shape, orientation, texture etc. Typically, coding of the visual feature is defined, which is invariant to location in the image. The user ....

A. Ono, M. Amano, M. Hakaridani, T. Satou, and M. Sakauchi. A flexible contentbased image retrieval system with combined scene description keyword. In Proceedings of the International Conference on Multimedia Computing and Systems, pages 201--208, 1996.

....to thousands of words in general[13] Therefore, alternatives may be image retrieval systems capable of automatically indexing and searching images based on the characteristics of their content. The main properties of images used for indexing are color, texture, shape and spatial relationships. 1][11][13] featured such attempts to index images in terms of the properties. For example, Photobook[13] is a set of interactive tools that allows content based search with appearance, 2 D shape, and combination of texture models such as reaction diffusion, Markov random field and a new wold model. ....

Ono, A., Amano, M., Hakaridani, M., Satou, T., Sakauchi, M.,"A Flexible Content-Based Image Retrieval System with Combined Scene Description Keyword," Proceedings of IEEE Multimedia96, pp. 201-208, 1996.

....modify his or her query accordingly. The distinct feature of CAETIIML (http: www.videolib.princeton.edu test retrieve) built at Princeton University, is its combination of the on line similarity searching and off line subject searching [60] More Image Retrieval systems can be found in [49, 10, 131, 154, 51, 168, 33, 153, 101]. 5. FUTURE RESEARCH DIRECTIONS From the above review, we can see that many advances have been made in various research aspects, including visual feature extraction, multi dimensional indexing, system design, etc. 148, 6, 66, 65, 67, 69, 70] However, there are still many open research issues ....

Atsushi Ono, Masashi Amano, and Mituhiro Hakaridani. A flexible content-based image retrieval system with combined scene descrption keyword. In Proc. IEEE Conf. on Multimedia Computing and Systems, 1996.

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A.Ono, M.Amano, M.Hakaridani, T.Satou and M.Sakauchi: "A flexible content-based image retrieval system with combined scene description keyword," Proc. IEEE Computer Society, International Conference on Multimedia Computing and Systems '96, pp. 201-208, 1996.

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