An image retrieval with color and texture features of image sub-blocks

Kavitha Chaduvula

ISBN 10: 3639713249 / ISBN 13: 9783639713244

Edité par Scholars' Press, 2014

An image retrieval with color and texture features of image sub-blocks | Kavitha Chaduvula | Taschenbuch | 168 S. | Englisch | 2014 | Scholars' Press | EAN 9783639713244 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand. N° de réf. du vendeur 105339592

Synopsis :

Each image is partitioned into 4×6 grids of equal-sized sub-blocks. The size of the sub-block is maintained as 64x64 pixels. Further the size of the sub-block is fixed for all the images. Then the color and texture features of each sub-block are computed. A color feature descriptor Local AutoCorrelogram (LAC) which is invariant to translation and occlusion is proposed to represent the color of the sub-block. Similarly, the texture of the sub-block is extracted based on Edge Oriented Gray Tone Spatial Dependency Matrix (EOGTSDM) of an image. An image matching scheme based on Integrated Minimum Cost Sub-block Matching (IMCSM) principle is used to compare the query and the target image, which in turn reduces the cost of finding the integrated matching distance. The adjacency matrix of a bipartite graph is formed using the sub-blocks of query and target image, which is used for matching the images. To further improve the quality of retrieval, a Relevance Feedback approach based on a feature re-weighting scheme is used to improve the retrieval accuracy. The experimental results show that this method has improved retrieval precision and recall.

Présentation de l'éditeur: Each image is partitioned into 4×6 grids of equal-sized sub-blocks. The size of the sub-block is maintained as 64x64 pixels. Further the size of the sub-block is fixed for all the images. Then the color and texture features of each sub-block are computed. A color feature descriptor Local AutoCorrelogram (LAC) which is invariant to translation and occlusion is proposed to represent the color of the sub-block. Similarly, the texture of the sub-block is extracted based on Edge Oriented Gray Tone Spatial Dependency Matrix (EOGTSDM) of an image. An image matching scheme based on Integrated Minimum Cost Sub-block Matching (IMCSM) principle is used to compare the query and the target image, which in turn reduces the cost of finding the integrated matching distance. The adjacency matrix of a bipartite graph is formed using the sub-blocks of query and target image, which is used for matching the images. To further improve the quality of retrieval, a Relevance Feedback approach based on a feature re-weighting scheme is used to improve the retrieval accuracy. The experimental results show that this method has improved retrieval precision and recall.

Détails bibliographiques

Titre : An image retrieval with color and texture ...
Éditeur : Scholars' Press
Date d'édition : 2014
Reliure : Taschenbuch
Etat : Neu