Content-based image indexing and searching using Daubechies' wavelets (original) (raw)
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Wavelet-based image indexing techniques with partial sketch retrieval capability
adl, 1997
This paper describes WBIIS Wavelet-Based Image Indexing and Searching, a new image indexing and retrieval algorithm with partial sketch image searching capability for large image databases. The algorithm characterizes the color variations over the spatial extent of the image in a manner that provides semanticallymeaningful image comparisons. The indexing algorithm applies a Daubechies' wavelet transform for each of the three opponent color components. The wavelet coe cients in the lowest few frequency bands, and their variances, are stored a s f e ature v e ctors. To s p eed u p retrieval, a two-step procedure is used that rst does a crude selection based on the variances, and then renes the search by performing a feature v e ctor match between the selected images and the query. For better accuracy in searching, two level multiresolution matching may also be used. Masks are used for partialsketch queries. This technique performs much better in capturing coherence of image, object granularity, local color texture, and bias avoidance than traditional color layout algorithms. When tested on a database of more than 10,000 general-purpose images, WBIIS is much faster and more a c curate than traditional algorithms.
Original articles Content-based image indexing and searching using Daubechies' wavelets
1997
This paper describes WBIIS (Wavelet-Based Image Indexing and Searching), a new image indexing and retrieval algorithm with partial sketch image search- ing capability for large image databases. The algorithm characterizes the color variations over the spatial extent of the image in a manner that provides semantically meaningful image comparisons. The indexing algorithm applies a Daubechies' wavelet transform for each of the three opponent color components. The wavelet coeÅ- cients in the lowest few frequency bands, and their variances, are stored as feature vectors. To speed up retrieval, a two-step procedure is used that first does a crude selection based on the variances, and then refines the search by performing a feature vector match between the selected images and the query. For better accuracy in searching, two-level multiresolution matching may also be used. Masks are used for partial-sketch queries. This technique performs much better in capturing coherence of image, ob...
Scalable color image indexing and retrieval using vector wavelets
IEEE Transactions on Knowledge and Data Engineering, 2001
This paper presents a scalable content-based image indexing and retrieval system based on vector wavelet coefficients of color images. Highly decorrelated wavelet coefficient planes are used to acquire a search efficient feature space. The feature space is subsequently indexed using properties of all the images in the database. Therefore the feature key of an image does not only correspond to the content of the image itself but also how much the image is different from the other images being stored in the database. The search time linearly depends on the number of images similar to the query image and is independent of the database size. We show that in a database of 5000 images, query search takes less than 30 msec, on a 266 MHz Pentium II processor compared to several seconds of retrieval time in the earlier systems proposed in the literature.
Complex Wavelet Transform-Based Color Indexing for Content-Based Image Retrieval
2004
With the rapid establishment of digital libraries and multimedia databases, the need for an efficient search algorithm is also increasing. In this paper, a new approach for content-based image indexing and retrieval is presented. The proposed method is based on a combination of multiresolution analysis and color characteristics of the image. Also, in order to obtain better retrieval results, the image texture features are combined with the color features to form a powerful discriminating feature vector for each image. The texture features are obtained with the use of dual-tree complex wavelet transform (DT CWT) method. According to the new algorithm, the image is divided into different sublayers, each of which containing only pixels in areas with similar spatial frequency characteristics. Here, we present a computationally efficient algorithm to implement an efficient content-based image retrieval algorithm. Simulation results show the efficiency of the proposed algorithm.
Multiresolution Wavelet Decompositions for Content Based Image Retrieval
… Journal of Computer …, 2010
We present a method for searching in an image database using a query image that is similar to the intended target. The query image may be a handdrawn sketch or a potentially low-quality) scan of the image to be retrieved. Our searching algorithm makes use of multiresolution wavelet decompositions of the query and database images. The coefficients of these decompositions are distilled into small "signatures" for each image. We introduce an "image querying metric" that operates on these signatures. This metric essentially compares how many significant wavelet coefficients the query has in common with potential targets. The metric includes parameters that can be tuned, using a statistical analysis, to accommodate the kinds of image distortions found in different types of image queries. The resulting algorithm is simple, requires very little storage overhead for the database of signatures, and is fast enough to be performed on a database of 20,000 images at interactive rates (on standard desktop machines) as a query is sketched. Our experiments with hundreds of queries in databases of 1000 and 20,000 images show dramatic improvement, in both speed and success rate, over using a conventional L1, L2, or color histogram norm.
Image Retrieval Based on the Wavelet Features of Interest
2006 IEEE International Conference on Systems, Man and Cybernetics, 2006
This paper presents a content-based image retrieval method based on the discrete wavelet transform (DWT). Due to the superiority in multiresolution analysis and spatial-frequency localization, the DWT is used to extract wavelet features (i.e., approximations, horizontal details, vertical details, and diagonal details) at each resolution level. Based on the observation that the YUV color space is rather effective in terms of the extraction of color features, each image is first transformed from the standard RGB color space to the YUV space, and then each component (i.e., Y, U, and V) of the image is further transformed to the Wavelet domain. In the image database establishing phase, the wavelet coefficients of each image are stored; in the image retrieving phase, the system compares the most significant wavelet coefficients of the Y, U, and V components of the query image with those of the images in the database, coupled with the weight factors assigned by users, and find out the matches based on the users' interested features. Experimental results demonstrate the effectiveness of our system.
Color and Texture Features for Image Indexing and Retrieval
2009 IEEE International Advance Computing Conference, 2009
computed and stored to construct indexing feature The novel approach combines color and texture vectors. The Gabor wavelets are a group of features for content based image retrieval (CBIR). wavelets, with each wavelet capturing energy at a The color and texture features are obtained by specific frequency and a specific direction. computing the mean and standard deviation on Expanding a signal using this basis provides a each color band of image and sub-band of different localized frequency description, therefore wavelets. The standard Wavelet and Gabor capturing local features/energy of the signal. wavelet transforms are used for decomposing the Texture features can then be extracted from this image into sub-bands. The retrieval results group of energy distributions. The scale obtained by applying color histogram (CH) + (frequency) and orientation tunable property of Gabor wavelet transform(GWT) to a 1000 image Gabor filter makes it especially useful for database demonstrated significant improvement in constructing indexing feature vectors. precision and recall, compared to the color
Lecture Notes in Computer Science, 2005
Content description and representation are still challenging issues for the design and management of content-based image retrieval systems. This work proposes to derive content descriptors of color images by wavelet coding and indexing of the HSV (Hue, Saturation, Value) channels. An efficient scheme for this problem has to trade between being translation and rotation invariant, fast and accurate at the same time. Based on a diverse and difficult database of 1020 color images, and a strong experimental protocol we propose a method that first divides an image into 9 rectangular regions (i.e. zoning), second it applies a wavelet transformation in each of the HSV channels. A subset of the approximation and of detail coefficients of each set is then selected. A similarity measure based on histogram intersection followed by vector distance computation for the 9 regions then evaluates and ranks the closest images of the database by content. In this paper we give the details of the this new approach and show promising results upon extensive experiments performed in our lab.
Indexing and Images Retrieval by Content
The indexing and images retrieval by content is a very interesting topic, and recently required in our days. In this paper, we propose a new structure of indexing and images retrieval process based on feature extraction by wavelet networks moralization and intelligent method of measuring similarity. First, each query image is modeled by a wavelet network of hybrid and optimal architecture, then, for the determination of visual characteristics called low levels such as: shape, texture, color, a detection filter based on wavelet shape is proposed and a new algorithm for measuring similarity between two color images is created in which the idea is inspired by a simple set of drawing colored balls. Thus, the proposed filter is used in determining the shape descriptor and accurately in the phase of binarization of the image request, whereas our algorithm of colors is applied in the act of measuring degree of similarity of two color images. Finally, for the determination of descriptors to those closest to the query image, we propose a new technique of comparison and decision-making of membership.