Human Perception based Image Retrieval using Emergence Index and Fuzzy Similarity Measure

The main concern dealing with content-based image retrieval (CBIR) is to bridge the semantic gap. The high level query posed by the user and low level features extracted by the machine illustrates the problem of semantic gap. To solve the problem of semantic gap, this paper presents a hybrid technique using an emergence index and fuzzy logic for efficient retrieval of images based on the colour feature. Emergence index (EI) is proposed to understand the hidden meaning of the image. Fuzzy similarity measure is developed to calculate the similarity between the target image and the images in the database. The images were ranked based on their similarity along with the fuzzy similarity distance measure. The preliminary experiments conducted on small set of images and promising results were obtained.

[1]  Cyrus Shahabi,et al.  Image retrieval by shape: a comparative study , 2000, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532).

[2]  Wee Kheng Leow,et al.  Fuzzy semantic labeling for image retrieval , 2004, 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763).

[3]  Jae Dong Yang.  An image retrieval model based on fuzzy triples , 2001, Fuzzy Sets Syst..

[4]  Anca L. Ralescu,et al.  Fuzzy hamming distance in a content-based image retrieval system , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[5]  Hayit Greenspan,et al.  Finding Pictures of Objects in Large Collections of Images , 1996, Object Representation in Computer Vision.

[6]  Sagarmay Deb.  Concepts of Emergence Index in Image Databases , 2005, Encyclopedia of Information Science and Technology.

[7]  Nozha Boujemaa,et al.  Using Fuzzy Histograms and Distances for Color Image Retrieval , 2000 .

[8]  Anca L. Ralescu,et al.  Image Clustering for a Fuzzy Hamming Distance Based CBIR System , 2005 .

[9]  Ramin Zabih,et al.  Comparing images using color coherence vectors , 1997, MULTIMEDIA '96.

[10]  Yixin Chen,et al.  A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  William I. Grosky,et al.  Idea Grou p Inc . Copy right Idea Grou p Inc . Copy right Idea Grou p Inc . Copy right Idea Grou p Inc . Chapter II Bridging the Semantic Gap in Image Retrieval , 2018 .

[13]  Kai-Kuang Ma,et al.  Fuzzy color histogram and its use in color image retrieval , 2002, IEEE Trans. Image Process..

[14]  Brijesh Verma,et al.  Fuzzy logic based interpretation and fusion of color queries , 2004, Fuzzy Sets Syst..

[15]  Markus A. Stricker,et al.  Similarity of color images , 1995, Electronic Imaging.

[16]  Yanchun Zhang,et al.  Retrieval of Geographic Location with Emergence Index in Multimedia , 2002, Joint Conference on Information Sciences.

[17]  Zhongfei Zhang,et al.  A clustering based approach to efficient image retrieval , 2002, 14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings..