Estimation of Similarity of Image Data Using Associative Memory and Its Applications
| Project/Area Number |
63580029
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
Informatics
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| Research Institution | Ehime University |
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Principal Investigator |
MURAKAMI Kenji Ehime Univ., Dep. of Electronics Eng., Associate Professor, 工学部, 助教授 (30036446)
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| Project Period (FY) |
1988 – 1989
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| Project Status |
Completed (Fiscal Year 1989)
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| Budget Amount *help |
¥1,800,000 (Direct Cost: ¥1,800,000)
Fiscal Year 1989: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 1988: ¥1,000,000 (Direct Cost: ¥1,000,000)
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| Keywords | Associative Memory / Image Database / Information Retrieval / Neural Network / Hopfield Model / Similarity Measure / Error Back Propagation / Singular Value Decomposition / 分散型記憶 / 内容参照記憶 / 画像情報 / 画像検索 |
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| Research Abstract |
The results of the project are summarized in the following five points:
(1) A new associative memory model which realizes both the stable and minimum error association for degraded input image data is proposed. The model is constructed based on the image restoration model. The performance of the model is also analyzed theoretically.
(2) In the Moore-Penrose generalized inverse associative memory, image data are memorized distributively in a matrix form. When a large scale image database is constructed by combining small scale databases, it is necessary to consolidate the memorized matrices. Using the property of orthonormal projection, a method to consolidate the matrices is introduced. From a theoretical analysis, we show the relation between the estimation ability of similarity and the number of memorized prototype image data.
(3) An orthogonal transformation method of energy function in Hopfield network is proposed. In programming a network, the energy functions are defined by a linear
… More
combination of an objective function and constraints. Since the combination causes interference, the solution would lack correctness or validity. The orthogonal transformation method improves this kind of badness by redefining the function in another basis. We verify the method can work for a Fuzzy Clustering of image data.
(4) In conventional image database, in order to estimate the similarity of two images, some features are extracted from them. Typical features which are often used in conventional image database are selected, and some properties of the features are given.
(5) A simple method of full color image data compression technique that significantly economizes on the number of bits required for an image is proposed. The proposed method is based on the multi-valued color dithering method and the conventional Popularity Algorithm. In the method, due to the effect of dither and the ensuing adequate color selection, both color reproduction and spatial resolution is easily obtained, and strong contouring is also suppressed. Less
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Report
(3 results)
Research Products
(19 results)
