Research On The Local Invariant Feature Extraction Of Images And Its Application

The extraction of local invariant feature in the target is the research fundament in computer vision field,for example image processing,digital watermarking,copy detection and video retrieval.Because there are generalized affine transformations,such as viewpoint, scale,rotation,blur,partial occlusion and complex background,among most of targets,when generalized affine transformation occurred,it is concentrated in vision field to make the local invariant feature of good stability,repeatability and matching.With respect to the shortcomings of high time complexity,regions overlapping and shape irregularity for original Maximally Stable Extremal Regions detector,the dissertation constructs a detector in parallel use of optimization neighborhood quadtree data structures, maximally stability criterion based on component tree and shape modification general expression with second order central moment based on vector;and then extracts Elliptical Maximally Stable Extremal Regions-Accelerative(EMSER-A).Based on pixel ordering, optimization neighborhood quadtree with path compression and union by rank are used to extract extremal regions based on intensity thresholds changing,which efficiently restore all of the information in the region that finally turned into one pixel and one intensity threshold. Extremal regions are used as nods to construct component tree,and maximally stability criterion is obtained by moving in the tree.For the convenience of the description for feature region,it establishes shape modification general expression with second order central moment based on vector,and reduces the dimensionality of the general expression to 2D covariance matrix,modifies irregularly regions to ellipse.Under the condition of ensuring repeatability, the EMSER-A recedes time complexity from O(nloglogn) to O(Na(N)).The experiments of extraction and matching show that,even on the occasions of generalized affine transformations,the detector can extract numerous distinctive local feature.The comparison experiment in repeatability proves that EMSER-A has the invariant in viewpoint,scale, rotation,brightness and blur.For resolving the problems including the lower location precision of the feature point, difficulty to modifying affine shape of the region and the poor affine invariance of the feature, the dissertation also constructs the detector based on point regions.It demonstrates the scale invariance of normalized Laplacian-of-Gaussian image derivatives,which provides theoretical fundament for characteristic scales.It presents qualitative and quantitative definition,also further improves and develops the properties of characteristic scales.It systematically testifies to the affine invariance of shape-adapted matrix and the rotation relation between the two normalized regions based on the matrix.It establishes the determining principles of the integration scale and the differentiation scale,and analyzes the impact of differentiation scale on the antinoise performance.It builds the space location iterative matrix based on affine shape-adapted matrix and realizes the conversion from the normalized regions to the image domain.With the support of theories mentioned above,Location/Scale/Shape-Iterative(LS~2-I) is constructed combined with the normalized Laplacian-of-Gaussian,multi-scale Harris measure,affine shape-adapted matrix and space location iterative matrix,and synchronously iterates the space location,characteristic scales and affine neighborhood shape of point regions.The experiments of the extraction and the matching show that,when extracting feature regions in the two images having the same scene with the generalized affine transformation,through the synchronous iterate in location,scale and shape,the neighborhood of convergence feature points in normalized image regions has the same content and satisfactory matching results are received.The comparison experiment in repeatability demonstrates that,under the changes of viewpoint,scale,rotation,brightness and blur,LS~2-I has good characteristics of repeatability.To testify the matching and stability of EMSER-A and LS~2-I,the paper constructs relevant image retrieval mechanism.Firstly,SIFT descriptors are produced in EMSER-A and LS2-I which are used as local feature regions at low level and then cluster into the visual vocabulary based on vector-quantized,by standard weighting and similarity rule for measuring invariant feature,query region is selected by the rectangle in the retrieval image. Finally,the similarity score is obtained according to which the image results are ranked at the first.To make full use of the invariance of the feature regions,it presents the spatial consistency measurement rule based on regions matching method in searching unit and family-based.The principles of the former are as follows:in terms of affine covariance of the elliptical feature regions of EMSER-A and LS~2-I,in the object regions and the retrievaling image the two elliptical regions which have matched are respectively taken as searching units where the regions are matched based on the relation between the original matching and the new matching,and delete the region which has the zero score.The later is on the basis of the relations among the diffirent correct matching pairs;we propose a filtration method to remove false matching:family-based spatial consistency filtration.At last,the frenquency ranking is weighted to be the spatial consistency reranking.The dissertation establishes four different retrieval mechanisms.Retrieval experiments prove the matching of EMSER-A and LS~2-I,and validity of the retrieval mechanism based on the object region and the spatial consistency. Meanwhile,comparison experiment by enlarging the capacity of images database and the experiment of the generalized retrieval demonstrate stability and correctness of the two feature regions and retrieval mechanism respectively.