Name: An Image Retrieval With Color and Texture Features of Image Sub-Blocks (en Inglés)
SKU: 46595064
Price: 293.29 PEN
Availability: InStock
Author: Chaduvula Kavitha
ISBN: 9783639713244

portada An Image Retrieval With Color and Texture Features of Image Sub-Blocks (en Inglés)

An Image Retrieval With Color and Texture Features of Image Sub-Blocks (en Inglés)

Formato

Libro Físico

Editorial

Blues Kids Of Amer

Autor

Chaduvula Kavitha

Año

2014

Idioma

Inglés

N° páginas

168

Encuadernación

Tapa Blanda

ISBN13

9783639713244

Categorías

Tecnología de la información: cuestiones generales

An Image Retrieval With Color and Texture Features of Image Sub-Blocks (en Inglés)

Chaduvula Kavitha (Autor) · Blues Kids Of Amer · Tapa Blanda

Libro Nuevo Importado

Envío: 14 a 18 días háb.

S/ 586,57S/ 293,29

-50%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 40 unidades

S/ 293,29

Llega entre el 23 Nov y el 30 Nov a CERCADO DE LIMA, Lima. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "An Image Retrieval With Color and Texture Features of Image Sub-Blocks (en Inglés)"

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.