We present algorithms for automatic image annotation and retrieval based on color, shape, texture, and any combination of two or more of these features. Pixel-or region (object)-based color; region-based shape; and block-or region-based texture features have been considered. Automatic region selection has been accomplished by integrating color and spatial edge features. Color, shape, and textur...

Nowadays, problem of shape and texture for 3D retrieval is still a challenge research. Although several methods exist, but we still have a space to improve the performance. In this paper, we aim to improve our previous 3D shape features and inserting texture features. We first do pose normalization as a process of adjusting the size, location, and orientation of a given object in a canonical sp...

In this paper, we present a new and effective color image retrieval scheme for combining all the three i.e. color, texture and shape information, which achieved higher retrieval efficiency. Firstly, the image is predetermined by using fast color quantization algorithm with clusters merging, and then a small number of dominant colors and their percentages can be obtained. Secondly, the spatial t...

In industrial applications, product identification is the most common thing now days. To kept in mind that we focus on the classification of our industrial product with the help of its texture using segmentation [7] and offset. Texture plays an important role in identifying the characteristics of an image/product. Image has visual features which are characterized as: (i) domain specific feature...

Content based image retrieval (CBIR) is an effective method of retrieving images from large image resources. CBIR is a technique in which images are indexed by extracting their low level features like, color, texture, shape, and spatial location, etc. Effective and efficient feature extraction mechanisms are required to improve existing CBIR performance. This paper presents a novel approach of ...

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation and ...

The potential of hand shape and hand texture-based biometry is investigated and algorithms are developed. Feature extraction stage is preceded by meticulous registration of the deformable shape of the hand. Alternative features addressing hand shape and hand texture are compared. Independent component analysis features prove to be the best performing in the identification and verification tasks...

This article presents a novel method for classification of plants using their leaves. Most plant species have unique leaves which differ from each other by characteristics such as the shape, colour, texture and the margin. The method introduced in this study proposes to use two of these features: the shape and the texture. The shape-based method will extract the contour signature from every lea...

Several researches in leaf identification did not include color information as features. The main reason is caused by a fact that they used green colored leaves as samples. However, for foliage plants—plants with colorful leaves, fancy patterns in their leaves, and interesting plants with unique shape—color and also texture could not be neglected. For example, Epipremnum pinnatum ‘Aureum’ and E...

The aim of this project is to classify the mammographic masses as benign or malignant using texture and shape features. A set of 73 mammograms is used for the analysis, out of which 41 are benign and 32 are malignant. Manually segmented masses are obtained from the DDSM, USF database [2]. Texture and shape features are extracted from the manually segmented masses. Stepwise linear discriminant a...