2,735 research outputs found
Robust Adaptive Median Binary Pattern for noisy texture classification and retrieval
Texture is an important cue for different computer vision tasks and
applications. Local Binary Pattern (LBP) is considered one of the best yet
efficient texture descriptors. However, LBP has some notable limitations,
mostly the sensitivity to noise. In this paper, we address these criteria by
introducing a novel texture descriptor, Robust Adaptive Median Binary Pattern
(RAMBP). RAMBP based on classification process of noisy pixels, adaptive
analysis window, scale analysis and image regions median comparison. The
proposed method handles images with high noisy textures, and increases the
discriminative properties by capturing microstructure and macrostructure
texture information. The proposed method has been evaluated on popular texture
datasets for classification and retrieval tasks, and under different high noise
conditions. Without any train or prior knowledge of noise type, RAMBP achieved
the best classification compared to state-of-the-art techniques. It scored more
than under impulse noise densities, more than under
Gaussian noised textures with standard deviation , and more than
under Gaussian blurred textures with standard deviation .
The proposed method yielded competitive results and high performance as one of
the best descriptors in noise-free texture classification. Furthermore, RAMBP
showed also high performance for the problem of noisy texture retrieval
providing high scores of recall and precision measures for textures with high
levels of noise
A Structural Based Feature Extraction for Detecting the Relation of Hidden Substructures in Coral Reef Images
In this paper, we present an efficient approach to extract local structural color texture features for classifying coral reef images. Two local texture descriptors are derived from this approach. The first one, based on Median Robust Extended Local Binary Pattern (MRELBP), is called Color MRELBP (CMRELBP). CMRELBP is very accurate and can capture the structural information from color texture images. To reduce the dimensionality of the feature vector, the second descriptor, co-occurrence CMRELBP (CCMRELBP) is introduced. It is constructed by applying the Integrative Co-occurrence Matrix (ICM) on the Color MRELBP images. This way we can detect and extract the relative relations between structural texture patterns. Moreover, we propose a multiscale LBP based approach with these two schemes to capture microstructure and macrostructure texture information. The experimental results on coral reef (EILAT, EILAT2, RSMAS, and MLC) and four well-known texture datasets (OUTEX, KTH-TIPS, CURET, and UIUCTEX) show that the proposed scheme is quite effective in designing an accurate, robust to noise, rotation and illumination invariant texture classification system. Moreover, it makes an admissible tradeoff between accuracy and number of features
K-Space at TRECVid 2007
In this paper we describe K-Space participation in
TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance.
The first of the two systems was a ‘shot’ based interface,
where the results from a query were presented as a ranked
list of shots. The second interface was ‘broadcast’ based,
where results were presented as a ranked list of broadcasts.
Both systems made use of the outputs of our high-level feature submission as well as low-level visual features
Curved Gabor Filters for Fingerprint Image Enhancement
Gabor filters play an important role in many application areas for the
enhancement of various types of images and the extraction of Gabor features.
For the purpose of enhancing curved structures in noisy images, we introduce
curved Gabor filters which locally adapt their shape to the direction of flow.
These curved Gabor filters enable the choice of filter parameters which
increase the smoothing power without creating artifacts in the enhanced image.
In this paper, curved Gabor filters are applied to the curved ridge and valley
structure of low-quality fingerprint images. First, we combine two orientation
field estimation methods in order to obtain a more robust estimation for very
noisy images. Next, curved regions are constructed by following the respective
local orientation and they are used for estimating the local ridge frequency.
Lastly, curved Gabor filters are defined based on curved regions and they are
applied for the enhancement of low-quality fingerprint images. Experimental
results on the FVC2004 databases show improvements of this approach in
comparison to state-of-the-art enhancement methods
- …