&lt;title&gt;Tools for texture- and color-based search of images&lt;/title&gt;

Ma, Wei‐Ying; Deng, Yining; Manjunath, B.S.

doi:10.1117/12.274547

Cited by 59 publications

(42 citation statements)

References 11 publications

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“…The image database consists of 4000 images, distributed into 28 different categories. We present the retrieval result of the proposed algorithm in this paper and compare it with other two clustering based algorithms [3] and [4]. The proposed algorithm in this paper is called CSOP (Color-Spatial Optimal Matching).…”

Section: Resultsmentioning

confidence: 99%

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Optimal Matching of Images Using Combined Color Feature and Spatial Feature

Huang

Shi-jia

Wang

et al. 2006

Computational Science – ICCS 2006

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Abstract. In this paper 1 we develop a new image retrieval method based on combined color feature and spatial feature. We introduce an ant colony clustering algorithm, which helps us develop a perceptually dominant color descriptor. The similarity between any two images is measured by combining the dominant color feature with its spatial feature. The optimal matching theory is employed to search the optimal matching pair of dominant color sets of any two images, and the similarity between the query image and the target image is computed by summing up all the distances of every matched pair of dominant colors. The algorithm introduced in this paper is well suited for creating small spatial color descriptors and is efficient. It is also suitable for image representation, matching and retrieval.

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Section: Resultsmentioning

confidence: 99%

“…Ma et al utilized a vector quantization called Generalized Lloyd algorithm (GLA) [3] to quantize the RGB color space. Mojsilovic [4] proposed a new quantization scheme in the Lab space based on spiral lattice.…”

Section: Introductionmentioning

confidence: 99%

Optimal Matching of Images Using Combined Color Feature and Spatial Feature

Huang

Shi-jia

Wang

et al. 2006

Computational Science – ICCS 2006

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“…Tools for texture/color based search of images describe the work on developing an image segmentation algorithm which is useful for processing large and diverse collections of image data [6]. Machine learning techniques for ontology based leaf classification proposes an integrated approach for an ontologybased leaf classification system [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Species Classification of Aquatic Plants using GRNN and ANFIS

Abirami¹,

Ramalingam²,

Palanivel³

2012

IJCA

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This work presents a method for plant species identification using the images of flowers. It focuses on the stable features of flowers such as color, texture and shape. K-means clustering is used to extract the color features. Texture segmentation is done using texture filters. Edge detectors are used to trace the boundary of the image and hence the shape features. Color, texture and shape features are extracted from 400 images of flowers. Classification of plants into dry land plants and aquatic plants, the aquatic plant species into wet and marsh aquatic plants, wet aquatic plants into iridaceae and epilobium family and marsh aquatic plants into malvaceae and onagraceae family, the iridaceae family into babiana and crocus species, the family epilobium into canum and hirsutum, the family malvaceae into mallow and pavonia, the family onagraceae into fuschia and ludwigia species are done using Generalized Regression Neural Network (GRNN) and Adaptive Neural Fuzzy Inference System (ANFIS) classifiers.

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“…Feature extraction -to derive relevant and effective features for automatic classification (Ma et al, 1997;Liu and Motoda, 1998a); Automatic classification -to achieve speed, accuracy, generalization, and automation; Active Learning -to use machine learning techniques to minimize expert intervention without performance deterioration; and Experimental evaluation -to compare the performance with and without the newly developed systems.…”

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confidence: 99%