Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking

Subrahmanyam, M. V. V.; Maheshwari, R. P.; Raman, Balasubramanian

doi:10.1016/j.sigpro.2011.12.005

Cited by 187 publications

(79 citation statements)

References 38 publications

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“…The Local Binary Patterns (LBP) is a simple description operator for the local texture (26), (27). Due to the low computational complexity and better ability for coding the details, the LBP is widely used in object tracking (28), (29).…”

Section: Motivationmentioning

confidence: 99%

Background Normalization and Texture Pattern-Based Video Segmentation for Visual Tracking

Rajagopal¹,

Sankaragomathi²

2017

Int. Res. J. Pharm.

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Visual tracking is the task of estimating the path of a target object in each frame of the video. Various tracking approaches are developed for tracking the position of the target. However, the tracker may fail to find the correct position, if the appearance of the target is similar to the background. This paper proposes a background normalization technique based on the textural pattern analysis to verify the matching of the features for the target region analysis. In this work, a novel model of background clustering is presented by using Multi-Weighted Chain Prediction (MWCP) algorithm for the uneven background. A Neighborhood Differential Binary Pattern (NDBP) is proposed to extract the texture for suppressing the shadow pixels in the image frame. From this equalized frame of the given video, the frame is split into several grids. From the grid format frame, the histogram features of the targeted frame are extracted, and each grid in that frame is classified. The Multi-Grid Weighted Classifier (MGWC) algorithm is used to find the matching of the grids to separate the background and foreground. This type of visual tracking system is robust over the sudden illumination changes and dynamic background. The experimental result proves that the proposed approach yields better precision, recall and F-score performance than the existing tracking approaches.

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Section: Motivationmentioning

confidence: 99%

Background Normalization and Texture Pattern-Based Video Segmentation for Visual Tracking

Rajagopal¹,

Sankaragomathi²

2017

Int. Res. J. Pharm.

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“…Success in terms of speed (no need to tune any parameters) and performance is reported in many research areas such as texture classification [17][18][19], face recognition [24][25][26], object tracking [30], biomedical image retrieval [31] and finger print recognition.…”

Section: Local Binary Patternsmentioning

confidence: 99%

“…Subrahmanyam et al [30] have proposed the local maximu m edge binary patterns (LM EBP) for image retrieval and object tracking applications. In proposed LM EBP for a given image the first maximu m edge is obtained by the magnitude of local difference between the center pixel and its eight neighbors as shown below:…”

Section: Local Maximum Edge Binary Patternsmentioning

confidence: 99%

“…Each pixel color in an image is then replaced by one color in the common colo r palette that is most similar to colo r so as to classify all pixels in image into k-cluster, called the CHKM feature. Subrahmanyam et al [30] have proposed the local maximu m edge binary patterns (LM EBP) for image retrieval and object tracking applicat ions. Their method extracts the features by taking into consideration the magnitude of local difference between the center pixel and its neighbors.…”

Section: Introductionmentioning

confidence: 99%

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Color and Local Maximum Edge Patterns Histogram for Content Based Image Retrieval

Jasmine¹,

Kumar²

2014

IJISA

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Abstract-In this paper, HSV color local maximum edge binary patterns (LM EBP) histogram and LM EBP joint histogram are integrated for content based image retrieval (CBIR). The local HSV region of image is represented by LM EBP, which are evaluated by taking into consideration the magnitude of local difference between the center pixel and its neighbors. This LM EBP differs from the existing LBP in a manner that it extracts the information based on distribution of edges in an image. Further the joint histogram is constructed between uniform two rotational invariant first three LM EBP patterns. The color feature is extracted by calculating the histogram on Hue (H), Saturation (S) and LM EBP histogram on Value (V) spaces. The feature vector of the system is constructed by integrating HSV LM EBP histograms and LM EBP joint histograms. The experimentation has been carried out for proving the worth of our algorithm. It is further mentioned that the databases considered for experiment are Corel-1K and Corel-5K. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to previously available spatial and transform domain methods on their respective databases.

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“…A local descriptor uses the visual features of regions or objects to describe the image, where as the global descriptor uses the visual features of the whole image. Several local descriptors have been described in the literature [17][18][19][20][21][22][23][24][25][26][27][28][29], where the local binary pattern (LBP) [17] is the most popular local feature descriptor.…”

Section: Introductionmentioning

confidence: 99%

Color Directional Local Quinary Patterns for Content Based Indexing and Retrieval

Vipparthi

Nagar

2014

Hum. Cent. Comput. Inf. Sci.

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This paper presents a novel evaluationary approach to extract color-texture features for image retrieval application namely Color Directional Local Quinary Pattern (CDLQP). The proposed descriptor extracts the individual R, G and B channel wise directional edge information between reference pixel and its surrounding neighborhoods by computing its grey-level difference based on quinary value (−2, −1, 0, 1, 2) instead of binary and ternary value in 0°, 45°, 90°, and 135°directions of an image which are not present in literature (LBP, LTP, CS-LBP, LTrPs, DExPs, etc.). To evaluate the retrieval performance of the proposed descriptor, two experiments have been conducted on Core-5000 and MIT-Color databases respectively. The retrieval performances of the proposed descriptor show a significant improvement as compared with standard local binary pattern LBP, center-symmetric local binary pattern (CS-LBP), Directional binary pattern (DBC) and other existing transform domain techniques in IR system.

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Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking

Cited by 187 publications

References 38 publications

Background Normalization and Texture Pattern-Based Video Segmentation for Visual Tracking

Background Normalization and Texture Pattern-Based Video Segmentation for Visual Tracking

Color and Local Maximum Edge Patterns Histogram for Content Based Image Retrieval

Color Directional Local Quinary Patterns for Content Based Indexing and Retrieval

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