Applications of Neighborhood Sequence in Image Processing and Database Retrieval (original) (raw)

Indexing and segmenting colour images using neighbourhood sequences

Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), 2000

In this paper we present some methods for indexing and segmenting colour images. The proposed procedures are based on well-known algorithms, but now we use digital distance functions generated by neighbourhood sequences to measure distance between colours. The application of such distance functions is quite natural and descriptive, since the colour coordinates of the pixels are non-negative integers. An additional interesting property of neighbourhood sequences that they do not generate metric in general, so we can obtain many distance functions in this way. We describe our methods for RGB images in details, but other image representations also could be considered. Moreover, the proposed methods can be applied in arbitrary dimensions without any difficulties.

A comparative analysis of two distance measures in color image databases

Proceedings. International Conference on Image Processing

Euclidean distance measure has been used in comparing feature vectors of images, while cosine angle distance measure is used in document retrieval. In this paper, we theoretically analyze these two distance measures based on feature vectors normalized by image size and experiment with them in the context of color image database. We find that the cosine angle distance, in general, works equally well for image databases. We show, for a given query vector, characteristics of feature vectors that will be favored by one measure but not by the other. We compute k-nearest neighbors for query images using both Euclidean and cosine angle distance for a small image database. The experimental data corroborate our theoretical results.

Distance measures for color image retrieval

1998

In this paper we address the issue of image database retrieval based on color using various vector distance metrics. Our system is based on color segmentation where only a few representative color vectors are extracted from each image and used as image indices. These vectors are then used with vector distance measures to determine similarity between a query color and a database image. We test numerous popular vector distance measures in our system and find that directional measures provide the most accurate and perceptually relevant retrievals.

A multidimensional query distance space for image retrieval by colour

A Multidimensional Query Distance Space (MQDS) is introduced for image retrieval. This is a querydependent space whose dimension is defined by the number of query colours. Specifically, the distances of the closest indexed colours to the query colours form a vector which lies in the MQDS. The location of this distance vector within the space and its relation to the origin and equidistant line, determines the overall ranking of a given image. The proposed scheme exhibits great flexibility. Querying can be performed in a number of ways, including query-by-colour and query-by-example and also allows incorporation of colour exclusion.

An efficient similarity measure (PMM) for color-based image retrieval

2008

Color histogram is an important Technical for color image database indexing and retrieving. However, the main problem with color histogram indexing is that it does not take the color spatial distribution into consideration. Previous researches have proved that the effectiveness of image retrieval increases when spatial feature of colors is included in image retrieval. In this paper, we introduce the local histogram to describe the spatial information of colors, to measure similarity between two images using the local histogram, the traditional approaches use the distance 1 L . To improve performance of the CBIR system, the permuto-metric measure (PMM) is used to measure similarity of images instead of classic distance 1 L .

Color-Based Image Retrieval Using Perceptually Modified Hausdorff Distance

EURASIP Journal on Image and Video Processing, 2008

In most content-based image retrieval systems, the color information is extensively used for its simplicity and generality. Due to its compactness in characterizing the global information, a uniform quantization of colors, or a histogram, has been the most commonly used color descriptor. However, a cluster-based representation, or a signature, has been proven to be more compact and theoretically sound than a histogram for increasing the discriminatory power and reducing the gap between human perception and computer-aided retrieval system. Despite of these advantages, only few papers have broached dissimilarity measure based on the cluster-based nonuniform quantization of colors. In this paper, we extract the perceptual representation of an original color image, a statistical signature by modifying general color signature, which consists of a set of points with statistical volume. Also we present a novel dissimilarity measure for a statistical signature called Perceptually Modified Hausdorff Distance (PMHD) that is based on the Hausdorff distance. In the result, the proposed retrieval system views an image as a statistical signature, and uses the PMHD as the metric between statistical signatures. The precision versus recall results show that the proposed dissimilarity measure generally outperforms all other dissimilarity measures on an unmodified commercial image database.

A Study of Distance Metrics in Histogram Based Image Retrieval

There has been a profound expansion of digital data both in terms of quality and heterogeneity. Trivial searching techniques of images by using metadata, keywords or tags are not sufficient. Efficient Content-based Image Retrieval (CBIR) is certainly the only solution to this problem. Difference between colors of two images can be an important metric to measure their similarity or dissimilarity. Content-based Image Retrieval is all about generating signatures of images in database and comparing the signature of the query image with these stored signatures. Color histogram can be used as signature of an image and used to compare two images based on certain distance metric. In this study, COREL Database is used for an exhaustive study of various distance metrics on different color spaces. Euclidean distance, Manhattan distance, Histogram Intersection and Vector Cosine Angle distances are used to compare histograms in both RGB and HSV color spaces. So, a total of 8 distance metrics for comparison of images for the sake of CBIR are discussed in this work.

Efficient image retrieval with multiple distance measures

1997

abstract There is a growing need for the ability to query image databases based on image content rather than strict keyword search. Most current image database systems that perform query by content require a distance computation for each image in the database. Distance computations can be time consuming, limiting the usability of such systems. There is thus a need for indexing systems and algorithms that can eliminate candidate images without performing distance calculations.

Content Based Image Retrieval Through Distance Similarity Metrics

Searching Test Image from Image databases using features extraction from the content is currently an active research area. In this work we present novel feature extraction approaches for content-based image retrieval when the query image is color image. To facilitate robust man-machine interfaces, we accept query images with color attributes. Special attention is given to the similarity measure with different distance matrices properties since the Test Image and Object Image from database finding the distance measuring. Several applicable techniques within the literature are studied for these conditions. The goal is to present the user with a subset of images that are more similar to the Object Image. One of the most important aspects of the proposed methods is that the accuracy measurement of the different database images. This significantly improves the feature extraction process and enables the methods to be used for other computer vision applications. https://sites.google.com/site/ijcsis/

An efficient color representation for image retrieval

IEEE Transactions on Image Processing, 2001

A compact color descriptor and an efficient indexing method for this descriptor are presented. The target application is similarity retrieval in large image databases using color. Colors in a given region are clustered into a small number of representative colors. The feature descriptor consists of the representative colors and their percentages in the region. A similarity measure similar to the quadratic color histogram distance measure is defined for this descriptor. The representative colors can be indexed in the three-dimensional (3-D) color space thus avoiding the high-dimensional indexing problems associated with the traditional color histogram. For similarity retrieval, each representative color in the query image or region is used independently to find regions containing that color. The matches from all of the query colors are then combined to obtain the final retrievals. An efficient indexing scheme for fast retrieval is presented. Experimental results show that this compact descriptor is effective and compares favorably with the traditional color histogram in terms of overall computational complexity.