SIFT vs other Feature Descriptor

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SIFT vs other Feature Descriptor

  1. 1. SIFT vs Other Feature descriptor Nisar Ahmed Rana
  2. 2. • SIFT is an algorithm in computer vision to detect and describe local features in images. • Feature description is obtained by extracting interesting points on the object in a training image. This description is used to identify the object in an image containing many other objects. • The relative positions between these features in the original scene shouldn't change from one image to another. For example, if only the four corners of a door were used as features, they would work regardless of the door's position; but if points in the frame were also used, the recognition would fail if the door is opened or closed.
  3. 3. • Scale Invariance • Rotation Invariance • Illumination Invariance • Viewpoint Invariance (Mostly) • Computationally Expensive • Variant to Light Color Changes • Variant to non-uniform Illumination (i.e. Shadows)
  4. 4. • The performance evolution of different local descriptors and their comparison with SIFT are summarized below: • SIFT and SIFT-like GLOH features exhibit the highest matching accuracies for an affine transformation of 50 degrees. After this transformation limit, results start to become unreliable. • Distinctiveness of descriptors is measured by summing the eigenvalues of the descriptors, obtained by the Principal components analysis of the descriptors normalized by their variance. This corresponds to the amount of variance captured by different descriptors, therefore, to their distinctiveness. PCA- SIFT (Principal Components Analysis applied to SIFT descriptors), GLOH (Gradient Location and Orientation Histogram) and SIFT features give the highest values.
  5. 5. • SIFT-based descriptors outperform other contemporary local descriptors on both textured and structured scenes, with the difference in performance larger on the textured scene. • For scale changes in the range 2-2.5 and image rotations in the range 30 to 45 degrees, SIFT and SIFT-based descriptors again outperform other contemporary local descriptors with both textured and structured scene content. • Introduction of blur affects all local descriptors, especially those based on edges, like shape context, because edges disappear in the case of a strong blur. But GLOH, PCA-SIFT and SIFT still performed better than the others. This is also true for evaluation in the case of illumination changes.
  6. 6. • SURF (Speeded Up Robust Features) is a robust local feature detector. It has shown to have similar performance to SIFT, while at the same time being much faster. • The standard version of SURF is several times faster than SIFT and claimed by its authors to be more robust against different image transformations than SIFT. SURF is based on sums of 2D Haar wavelet responses and makes an efficient use of integral images. • It uses an integer approximation to the determinant of Hessian blob detector, which can be computed extremely quickly with an integral image. For features, it uses the sum of the Haar wavelet response around the point of interest. Again, these can be computed with the aid of the integral image.
  7. 7. • LESH is a recently proposed image descriptor which can be used to get a description of the underlying shape. • The LESH feature descriptor is built on local energy model of feature perception. • It is designed to be scale invariant. • It encodes the underlying shape by accumulating local energy of the underlying signal along several filter orientations, several local histograms from different parts of the image/patch are generated and concatenated together into a 128-dimensional compact spatial histogram. • The LESH features can be used in applications like shape-based image retrieval, object detection, and pose estimation.
  8. 8. • GLOH is a robust image descriptor that can be used in computer vision tasks. • It is a SIFT-like descriptor that considers more spatial regions for the histograms. • The higher dimensionality of the descriptor is reduced to 64 through principal components analysis (PCA).

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