Regularization of Hidden Layer Unit Response for Neural Networks

In this paper, we looked into two issues in pattern recognition using neural networks trained by Back Propagation (BP), namely inefficient learning and insufficient generalization. We observed that these phenomena are partly caused by the way the hidden layer units responds to the inputs

Connecting Image Similarity Retrieval with Consistent Labeling Problem by Introducing a Match-all Label

Our work in image similarity retrieval by relaxation labeling processes required adding local constraints to obtain an initial set of compatible objects and labels on pairs of images to ensure labeling consistency at convergence. This approach suffers from the problem of over-specified constraints that leads to potentially invaluable information being prematurely removed. To address this problem, we introduce the idea of a Match-all label for objects that failed these constraints. It serves to give them a defined labeling probability as well as allowing them participate in global correspondences through the compatibility model. We show that this enhanced formulation still meets the conditions for a theorem on labeling consistency in Hummel and Zucker to be satisfied. At convergence, the set of objects and their most consistent labels constitute a best partial labeling for the pair of images

A New Method on Assigning Function Types to Line Segments for Function Approximation-based Image Coding

This paper proposes a new method to determine the types of function for approximating line segments using an algorithm based on binary search. In the function approximation-based image coding, it is essential that the joint points between different types of line segment be extracted precisely. Through experiments, it is verified that the proposed method extracts straight lines and arcs more precisely than the earlier method

Trademark Retrieval by Relaxation Matching on Fluency Function Approximated Image Contours

We propose an automatic retrieval method that addresses the problem of finding similar trademark images from a database when compared with an input. Our method is based on evaluating the compatibility of relative relations among extracted image contour segments between the input and the registered images using relaxation matching. The overall compatibility, after finding a best matching configuration between contour segments of the input and each registered image, provides the basis for a distance metric used in similarity ranking. Experiments were carried out on 300 randomly selected trademark images obtained from the Japan Patent Office's Website for performance evaluation. The applicability of our method, however, is not limited to trademark retrieval but can be extended to other applications having the need for image similarity matching

A Publishing System based on Fluency Coding Method

This paper proposes a new publishing system using PC and large-sized printer that can produce high quality printed matters comprising text, charts, and illustrations, etc. To construct this system, a novel coding method is needed to ensure the quality of decoded images even on Affine-transformed enlargement or reduction. Conventional coding methods, however, cannot claim to have satisfied such a requirement. This paper presents an algorithm for encoding and decoding binary images by adaptively approximating the contours using Fluency functions in the form of straight lines, arcs, and curved lines that were proposed by one of the authors. From the experimental results, we demonstrate that the quality of the decoded images was also maintained without the jaggy noises even on resizing. Furthermore, signboards produced by our publishing system have actually been used in several live events

A Method on Tracking Common Boundaries of Color Regions in function Approximation-based Image Coding

A boundary tracking method crucial to function approximation-based image coding is proposed that solves the problems caused by duplicate tracking the common boundaries of color regions when methods conventionally applied to bi-level raster images are inappropriately used. Experimental evaluation is performed to verify its effectiveness

Approximate Query Processing for a Content-Based Image Retrieval Method

An approximate query processing approach for a content-based image retrieval method based on probabilistic relaxation labeling is proposed. The novelty lies in the inclusion of a filtering mechanism based on a quasi lower bound on distance in the vector space that effectively spares the matching between the query and a number of database images from going through the expensive step of iterative updating the labeling probabilities. This resembles the two-step filter-and-refine query processing approach that has been applied to k-nearest neighbor (k-NN) retrieval in database research. It is confirmed by experiments that the proposed approach consistently returns a "close approximation" of the accurate result, in the sense of the first k' in the top k output of a k-NN search, while simultaneously reduces the amount of processing required

A Method on Tracking Unit Pixel Width Line Segments for Function Approximation-Based Image Coding

In this paper, we propose a novel method on tracking unit pixel width line segments for function approximation-based image coding. This method is applied prior to function approximation of line segments in image coding. Compared to conventional methods, our method overcomes the problems introduced by inaccurately tracking unit pixel width contours that appear normally in images of fine details such as maps and circuit diagrams. These problems include the inability to reproduce thin segments of uniform width and the separation of segments at visually unnatural places due to image enlargement. As an illustration of its effectiveness, we apply our method on a blank map image followed by image coding via function approximation