PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
A new content-based image retrieval technique using color and texture information
Computers and Electrical Engineering
Fast K-means algorithm based on a level histogram for image retrieval
Expert Systems with Applications: An International Journal
Content-based image retrieval by integrating color and texture features
Multimedia Tools and Applications
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This paper proposes three image features for use in image retrieval. The first image feature is based on color distribution and is called an adaptive color histogram (ACH). The second and third image features, called adaptive motifs co-occurrence matrix (AMCOM) and gradient histogram for adaptive motifs (GHAM), are based on color and texture features, respectively. ACH uses three steps, including cluster drop, auto cluster merge and auto cluster split to improve the K-mean algorithm. AMCOM is commonly used and is a generally conventional AMCOM. GHAM estimates the average gradient of pixels in a motif of pattern blocks and creates a histogram of the average gradient corresponding to motifs of pattern blocks. We integrate ACH, AMCOM and GHAM to form an AMCGH system to facilitate a more accurate and efficient image retrieval system. To present the performance of the proposed system, four different image databases, including gray texture images, two types of color texture images and color natural images, are tested and analyzed.