Content Based Image Retrieval Using Color, Texture and Shape Features

Basu, Jhankar; Sahidullah, M.; Sinha, Amitabha

doi:10.1109/adcom.2007.21

Cited by 194 publications

(156 citation statements)

References 20 publications

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“…The numbers in the Table 1, represent the average precision computed over the corresponding image class. From Table 1, it can be easily seen, that the proposed CBIR system provide improved retrieval performance over other existing CBIR algorithms namely, SIMPLIcity [17], FIRM [18], using salient points (salient points detected by Harris corner detector (SP by HCD), color salient points (CSP)) [19] and contourlet Harris detector (CHD) [20]. Only for the classes "Sea", "Building" and "Buses" the performance of CHD based method outperforms our method.…”

Section: Experimental Results and Comparisonmentioning

confidence: 99%

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Novel CBIR system based on Ripplet Transform using interactive Neuro-Fuzzy technique

Chowdhury

Das

Kundu

2012

ELCVIA

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Content Based Image Retrieval (CBIR) system is an emerging research area in effective digital data management and retrieval paradigm. In this article, a novel CBIR system based on a new Multiscale Geometric Analysis (MGA)-tool, called Ripplet Transform Type-I (RT) is presented. To improve the retrieval result and to reduce the computational complexity, the proposed scheme utilizes a Neural Network (NN) based classifier for image pre-classification, similarity matching using Manhattan distance measure and relevance feedback mechanism (RFM) using fuzzy entropy based feature evaluation technique. Extensive experiments were carried out to evaluate the effectiveness of the proposed technique. The performance of the proposed CBIR system is evaluated using a 2 × 5-fold cross validation followed by a statistical analysis. The experimental results suggest that the proposed system based on RT, performs better than many existing CBIR schemes based on other transforms, and the difference is statistically significant.

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Section: Experimental Results and Comparisonmentioning

confidence: 99%

“…The retrieval results obtained using the proposed CBIR system, are compared with some of the existing retrieval systems [20,17,18,19]. The experiments were carried out on a Dell Precision T7400 PC with 4GB RAM and was implemented using MATLAB R2008a.…”

Section: Experimental Results and Comparisonmentioning

confidence: 99%

Novel CBIR system based on Ripplet Transform using interactive Neuro-Fuzzy technique

Chowdhury

Das

Kundu

2012

ELCVIA

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“… Interpretation / evaluation: interpret the result/query to give meaningful report/ information. Various algorithms and techniques like Classification, Clustering, Regression, Artificial Intelligence, Neural Networks, Association Rules, Decision Trees, Genetic Algorithm, Nearest Neighbor method etc., are meant for knowledge discovery from databases [5]. The main objective of this paper learns about the data mining.…”

Section: Data Miningmentioning

confidence: 99%

“…D. N. D and Dr. Lalitha Bhaskari. D [5] discuss Image Retrieval, which is an important phase in image mining, is one technique which helps the users in retrieving the data from the available database. The fundamental challenge in image mining is to reveal out how low-level pixel representation enclosed in a raw image or image sequence can be processed to recognize high-level image objects and relationships.…”

Section: Shape Featurementioning

confidence: 99%

Image Retrieval Using Data Mining and Image Processing Techniques

Chouhan¹,

Tiwari²

2015

International Journal of Innovative Research in Electrical, Ele

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Abstract:In the domain of Image processing, Image mining is advancement in the field of data mining. Image mining is the extraction of hidden data, association of image data and additional pattern which are quite not clearly visible in image. It's an interrelated field that involves, Image Processing, Data Mining, Machine Learning, Artificial Intelligence and Database. The lucrative point of Image Mining is that without any prior information of the patterns it can generate all the significant patterns. This is the writing for a research done on the assorted image mining and data mining techniques. Data mining refers to the extracting of knowledge /information from a huge database which is stored in further multiple heterogeneous databases. Knowledge/ information is communicating of message through direct or indirect technique. These techniques include neural network, clustering, correlation and association. This writing gives an introductory review on the application fields of data mining which is varied into telecommunication, manufacturing, fraud detection, and marketing and education sector. In this technique we use size, texture and dominant colour factors of an image. Gray Level Co-occurrence Matrix (GLCM) feature is used to determine the texture of an image. Features such as texture and color are normalized. The image retrieval feature will be very sharp using the texture and color feature of image attached with the shape feature. For similar types of image shape and texture feature, weighted Euclidean distance of color feature is utilized for retrieving features.

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“…Lin et al [4] proposed a scheme based on color-texture and color-histogram. Hiremath and Pujari [5] came up with a CBIR technique based on color, shape and texture. Rao et al [6] proposed dominant color based retrieval system.…”

Section: Related Workmentioning

confidence: 99%

Image Retrieval using Perceptual Hashing

Sarohi¹

2013

IOSR-JCE

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Content Based Image Retrieval Using Color, Texture and Shape Features

Cited by 194 publications

References 20 publications

Novel CBIR system based on Ripplet Transform using interactive Neuro-Fuzzy technique

Novel CBIR system based on Ripplet Transform using interactive Neuro-Fuzzy technique

Image Retrieval Using Data Mining and Image Processing Techniques

Image Retrieval using Perceptual Hashing

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