Results 1 - 10 of 350

Multiresolution grayscale and rotation invariant texture classification with local binary patterns

by Timo Ojala, Matti Pietikäinen, Topi Mäenpää - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2002
"... This paper presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain ..."
Abstract - Cited by 1299 (39 self) - Add to MetaCart
that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and rotation invariant operator presentation that allows for detecting the "

Content-based image retrieval at the end of the early years

by Arnold W. M. Smeulders, Marcel Worring, Simone Santini, Amarnath Gupta, Ramesh Jain - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2000
"... The paper presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for imag ..."
Abstract - Cited by 1618 (24 self) - Add to MetaCart
for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof

Image Retrieval based on Local Histogram and Texture Features

by Ch. Kavitha, M. Babu Rao, Dr. B. Prabhakara Rao, Dr. A. Govardhan
"... Abstract — In this paper we propose a new and efficient technique to retrieve images based on sum of the values of Local Histogram and GLCM (Gray Level Co-occurrence Matrix) texture to enhance the retrieval performance. Most of the image retrieval techniques used Histograms for indexing. Histograms ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
for Local Histogram and Euclidean distance is adopted for texture features. Weighted combined distance is used in retrieving the images. The experimental results show that the proposed method has achieved highest retrieval performance.

A sparse texture representation using local affine regions

by Svetlana Lazebnik, Cordelia Schmid, Jean Ponce - IEEE Transactions on Pattern Analysis and Machine Intelligence , 2005
"... This article introduces a texture representation suitable for recognizing images of textured surfaces under a wide range of transformations, including viewpoint changes and non-rigid deformations. At the feature extraction stage, a sparse set of affine Harris and Laplacian regions is found in the im ..."
Abstract - Cited by 210 (15 self) - Add to MetaCart
image and the RIFT descriptor. When affine invariance is not required, the original elliptical shape serves as an additional discriminative feature for texture recognition. The proposed approach is evaluated in retrieval and classi-fication tasks using the entire Brodatz database and a publicly

Unsupervised texture segmentation using feature distributions

by Timo Ojala, Matti Pietikäinen - Pattern Recognition , 1999
"... This paper presents an unsupervised texture segmentation method, which uses distributions of local binary patterns and pattern contrasts for measuring the similarity of adjacent image regions during the segmentation process. Nonparametric log-likelihood test, the G statistic, is engaged as a pseudo- ..."
Abstract - Cited by 99 (4 self) - Add to MetaCart
This paper presents an unsupervised texture segmentation method, which uses distributions of local binary patterns and pattern contrasts for measuring the similarity of adjacent image regions during the segmentation process. Nonparametric log-likelihood test, the G statistic, is engaged as a pseudo

Periodicity, directionality, and randomness: Wold features for image modeling and retrieval

by F. Liu, R. W. Picard - IEEE Trans. Pattern Analysis and Machine Intelligence , 1996
"... One of the fundamental challenges in pattern recognition is choosing a set of features appropriate to a class of problems. In applications such as database retrieval, it is important that image features used in pattern comparison provide good measures of image perceptual similarities. In this paper, ..."
Abstract - Cited by 134 (5 self) - Add to MetaCart
One of the fundamental challenges in pattern recognition is choosing a set of features appropriate to a class of problems. In applications such as database retrieval, it is important that image features used in pattern comparison provide good measures of image perceptual similarities. In this paper

Second Order Statistical Texture Features from a New CSLBPGLCM for Ultrasound Kidney Images Retrieval

by Chelladurai Callins Christiyana , Vayana Perumal Rajamani , 2013
"... Abstract This work proposes a new method called Center Symmetric Local Binary Pattern Grey Level Cooccurrence Matrix (CSLBPGLCM) for the purpose of extracting second order statistical texture features in ultrasound kidney images. These features are then feed into ultrasound kidney images retrieval ..."
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Abstract This work proposes a new method called Center Symmetric Local Binary Pattern Grey Level Cooccurrence Matrix (CSLBPGLCM) for the purpose of extracting second order statistical texture features in ultrasound kidney images. These features are then feed into ultrasound kidney images retrieval

Texture Retrieval Using Ordinal Co-occurrence Features

by Mari Partio, Bogdan Cramariuc, Moncef Gabbouj , 2004
"... Due to variations in illumination conditions in texture evaluation applications, gray scale invariance is an important property in texture similarity evaluation. Using the order of the gray values instead of the gray values themselves is shown to improve the retrieval accuracy. Ordinal measures have ..."
Abstract - Cited by 3 (2 self) - Add to MetaCart
have been used for many image processing tasks in the literature. In this paper, we propose a novel combination of ordinal measures and cooccurrence matrices using local thresholding. Features constructed in this paper represent the occurrence frequency of certain ordinal relationships at different

Image Retrieval based on the Multiwavelets Texture-Spatial Features

by An Zhiyong, Li Jinjiang, Zhao Feng, Guo Jie, Yantai China , 2014
"... Abstract: A new retrieval algorithm based on the texture-spatial texture using the GHM multiwavelets is presented. In order to describe the important visual information of image, we design the improved multiwavelets quantization map that can depict the important visual information for the multiwavel ..."
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for the multiwavelets sub-bands. Furthermore, the visual spatial histogram of multiwavelets quantization map is used as the texture-spatial features that denote the global texture information of image. At the same time, the local binary pattern is used to describe the local texture-spatial feature for the low frequency

Constructing models for content-based image retrieval

by Cordelia Schmid - In Proc. CVPR , 2001
"... This paper presents a new method for constructing models from a set of positive and negative sample images; the method requires no manual extraction of significant objects or features. Our model representation is based on two layers. The first one consists of “generic ” descriptors which represent s ..."
Abstract - Cited by 133 (12 self) - Add to MetaCart
characteristic model features (common to the positive and rare in the negative examples) and increases the performance of the model. Models are retrieved and localized using a probabilistic score. Experimental results for “textured ” animals and faces show a very good performance for retrieval as well
Results 1 - 10 of 350