Book ChapterDOI
Local and Global Color Histogram Feature for Color Content-Based Image Retrieval System
Jyoti Narwade,Binod Kumar +1 more
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TLDR
In image retrieval applications, user specifies desired image as query image and wants to search for the most similar image in database of his interest, and application identifies similar relevant images from database based on different color features of database images and query image.Abstract:
Content-based image retrieval system nowadays use color histogram as a common color descriptor. We consider color as one of the important features during image representation process. Different transformations such as changing scale of image, rotating an image, and translations of image to other forms does not make any alterations to the color content of image. If we need to focus on differentiation or similarity between two images we usually deal with various color features of image. To extract color features of image we consider on color space, color reduction, color feature extraction process. In image retrieval applications, user specifies desired image as query image and wants to search for the most similar image in database of his interest. Application then identifies similar relevant images from database based on different color features of database images and query image. To achieve this we compute color features of database images and those for query image. We use local color features of different regions and combine them to represent color histogram as a color feature. These color features are compared using Euclidean distance as a metric to define similarity between the query image and the database images. For calculations of local color histogram we divide image into different blocks of size 8 × 8 as fixed, so that for each block of image spatial color feature histogram of image is obtained. Our experimental work shows that local hybrid color histogram produced more accurate image retrieval results than global color moments color histogram.read more
Citations
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Proceedings ArticleDOI
Hybridization of moth flame optimization and gravitational search algorithm and its application to detection of food quality
TL;DR: A hybrid optimization algorithm is presented by using concepts of moth flame optimization and gravitational search algorithm and applies this hybrid algorithm to image segmentation and results of the segmentation are used to classify apples into different classes.
Journal ArticleDOI
Fuzzy semi-supervised weighted linear loss twin support vector clustering
Reshma Rastogi,Aman Pal +1 more
TL;DR: To build a robust clustering algorithm which is not sensitive to noise and outliers, the proposed formulations achieve better clustering accuracy over other state-of-the-art plane-based clustering algorithms with comparatively lesser computational time.
Journal ArticleDOI
Categorizing Color Appearances of Image Scenes Based on Human Color Perception for Image Retrieval
TL;DR: The efforts in narrowing the gap between low relevancy human text descriptions for Malaysian users and image scene color appearances have been brought into attention and the agreement analysis indicates that the Bright category is the most comprehensible by humans and subsequently followed by the Pastel and Dark categories.
Proceedings ArticleDOI
Enhancement Of Segmentation And Feature Fusion For Apple Disease Classification
TL;DR: An approach for the apple disease classification with enhancement of defect segmentation and fusion of color, texture and shape based features is used to classify the apple into two types, healthy and defected with an accuracy of 96% using Histogram of Oriented Gradients feature descriptor and Bagged Decision Trees classifier.
DissertationDOI
Thermal and Visual Imaging and Accelerometry Developments to Assist with Arthritis Diagnosis
TL;DR: The study for the first time brought together the three techniques of thermal imaging, visual imaging and accelerometry to assist with JIA diagnosis and demonstrated that the developed techniques have potential in assisting clinicians with Jia diagnosis.
References
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Proceedings ArticleDOI
Histogram refinement for content-based image retrieval
Greg Pass,Ramin Zabih +1 more
TL;DR: It is demonstrated that histogram refinement can be used to distinguish images whose color histograms are indistinguishable, and a split histogram called a color coherence vector (CCV), which partitions each histogram bucket based on spatial coherence.
Proceedings ArticleDOI
Content-based image retrieval using color moment and Gabor texture feature
TL;DR: The proposed content-based image retrieval method has higher retrieval accuracy than conventional methods using color and texture features even though its feature vector dimension results in a lower rate than the conventional method.
Journal ArticleDOI
Color image retrieval technique based on color features and image bitmap
Tzu-Chuen Lu,Chin-Chen Chang +1 more
TL;DR: The color distributions, the mean value and the standard deviation are used to represent the global characteristics of the image for increasing the accuracy of the retrieval system and the proposed technique indeed outperforms other schemes in terms of retrieval accuracy and category retrieval ability.
Journal ArticleDOI
Color image quantization by minimizing the maximum intercluster distance
TL;DR: It is shown that getting the smallest clusters under a formal notion of minimizing the maximum intercluster distance does not guarantee an optimal solution for the quantization criterion, Nevertheless the use of an efficient clustering algorithm by Teofilo F. Gonzalez, which is optimal with respect to the approximation bound of the clustering problem has resulted in a fast and effective quantizer.
Proceedings ArticleDOI
Using Phase and Magnitude Information of the Complex Directional Filter Bank for Texture Image Retrieval
TL;DR: This paper discusses how to utilize both magnitude and phase information obtained from the complex directional filter bank (CDFB) for the purpose of texture image retrieval, and proposes a new feature vector called CDFB-RP.