Content-based Image Retrieval With Ant Colony Optimization

Content-based image retrieval with skin tones and shapes using Ant colony optimization

Introduction:

Due to the enormous pool of image data, a plenty of data to be sort out has lead the way for analyzing and dig up the data to acquire likely worthwhile information. Heterogeneous fields cover from commercial to military desire to inspect data in a systematic and quick manner. Outstandingly in the area of interactive media, images have the stronghold. There is no sufficient tools are available for examination of images. One of the points at issue is the effective pinpointing of features in the likeness and the other one is extracting them.

NEED AND IMPORTANCE OF RESEACH PROBLEM

Current techniques in image retrieval and classification concentrate on content-based techniques. It seek survey the contents of the image rather than thedata about datasuch as keywords, label or properties corresponding with the image. The term “content” refer to shades, appearance, textures, or any other particulars that can be obtained from the image itself. CBIR with skin tones is advisable because most net-based image search engines rely purely on metadata and this turn out a lot of waste in the results.Thus a system that can sifter images rest on their content with additional property i.e., skin tone would serve better list and return more specific outcomes. Various systems like the QBIC, Retrieval Ware and Photo Book etc., have a variety of attributes, still used in distinct discipline. The color features integrated with shape for classification, the color and texture for retrieval. There is no single feature which is ample; and, moreover, a single representation of characteristics is also not enough. Sonith et al.[1996] describes a fully automated content – based image query systems. Ioloni et al. [1998] describes image retrieval by color semantics with incomplete knowledge. Mori et al. [1999] have applied dynamic programming technique for function approximated shape representation. Chang et al. [2001] describes information driven framework for image. Mira et al. [2002] describes fact content based image retrieval using Qusi – Gabir filler Vincent et al. [2007] have developed a fully automated content based image query system. Heraw et al. [2008] describes image retrieval will an enhanced multi modeling ontology. Taba et al. [2009] have used mining association rules for the feature matrin.

OBJECTIVES

Moreover, speed changes in industry and databases influencing our view and understanding of the problem over time and demanding alter in problem decoding approach. Consequently, further research is required in this field to develop algorithms for pick out images with skin tone and shapes, able to cope with ongoing technological changes.

1. Investigation of effective images with skin tone and shapes based on pixel algorithms
2. Extracting them based on optimization algorithms.
3. Developing computational algorithms in extracting the images.

The main objective is to study the Image Identification and Optimistic method of Image Extraction for Image Mining using Ant colony optimization .ACO, good solutions to a given optimization problem. To achieve this main objective, the goals are formulated as follows:

• To Study the Image Mining Techniques.
• To Explore the Approaches used in Selecting the Images
• To Explore the Extracting of the Features.
• To apply the powerful Techniques.
• To Analyze the Experimental Results.
• To Study the Optimization Techniques.
• To bring down calculation and taking out time.

Work Plan:

I will begin my research work by investigating different methodologies available in the literature and measure their applicability in different perspectives for common benefit. After that, I prefer to limit my research interest down from general to even more specific under the guidance of designated supervisor in the course so that it fits into university doctoral program curriculum. The research tasks are grouped year wise as follows.

Year-1:

Literature survey on various methods to get an idea of pattern matching, shapes and classification.

Implementation of algorithms in order to gauge their applicability and scalability.

Mathematical modelling of Ant colony Optimization considering new objectives and constraints existing in Image processing.

Submission of a paper to a major conference

Develop a detailed research proposal and give oral defense to get full registration of the course

Year-2

Continue and refine the mathematical model to make the problem more actual

Develop single objective optimization algorithms for effective extraction of Images.

Start to develop multi objective optimization algorithms for extraction by considering large scale optimization and classification

Submission of two papers to international conference and journals

Year-3:

Implementation of developed algorithms for analysis of images and optimization problems

Submission of a paper to a major journal

Completing a thesis based on the PhD project

Taking part in active research groups.

Publication of research work.

REFFERENCES

1. Beyer K et al. [1999]: Bottom-Up computation of sparse and Iceberg CUBEs. ACM SIGMOD.
2. Carter R et al.[1983]: CIELUV color difference equations for self-luminoudisplays. Color Res. & Appl., 8(4), 252–553.
3. Chang SF et al. [1995]: Extracting multi-dimensional signal features for content-based visual query. SPIE Symposium on Visual Communications and Signal Processing.
4. idoni J et al. [1998]: Image retrieval by color semantics with incomplete knowledge. Journal of the American Society for Information Science, 49(3), 267-282.
5. evich V et al. [2008]: Medical Image Mining on the Base of Descriptive Image Algebras. Cytological Specimen Case. In : Proc.of the International Conference on Health Informatics—HEALTHINF, Funchal, Madeira, Portugal, 2, 66–73.
6. Huan et al.[2008]: Image Retrieval ++ — web Image Retrieval with an enhanced Multi-modality ontology . Kluwer Academic Publishers.
7. Jaba Sheela et al. [2009]: Image mining using association rules derived from feature matrix. ACM, 440-443.
8. Jain A [1991]: Algorithms for clustering data. Englewood Cliffs, NJ, Prentice Hall.
9. Jain A et al.[1996]: Image Retrieval using color and shape. Pattern Recognition, 29(8):1233-1244.
10. James D [1993]: Content based retrieval in multimedia imaging. In : Proc. SPIE Storage and Retrieval for Image and Video Databases.
11. Kantardzic M [2003]: Data Mining, Wiley-Interscience.
12. MaW et al.[1997]: Tools for texture/color based search of images. SPIE International conference – Human Vision and Electronic Imaging, 496-507.
13. Mira P et al.[2002]: Fast content-based image retrieval using quasi – gabor filter and reduction of image feature dimension. SSIAI, 178-182.
14. Mori K et al.[1999]: Function approximated shape representation using dynamic programming with multi-resolution analysis. ICSPAT 99.
15. Niblack W et al. [1994]: The QBIC project: Querying images by content using color, texture and shape. In : Proc. SPIE Storage and Retrieval for Image and Video Databases.
16. Pentland A et al. [1996]: Content based manipulation of databases. Int. J. Comput. Vis., 18(3), 233-254.
17. Rui Y et al. [1999]: Image retrieval: current techniques, promising directions and open issues. Journal of Visual Communication and Image Representation, 10(4), 39-62.
18. Shiaofen Fang et al. [2009]: Facial image classification of mouse embryos for the animal model study of Fetal Alcohol Syndrome. Proceedings of the 2009 ACM symposium on Applied Computing, 852-856.
19. Smith J et al. [1996]: VisualSEEK: A fully automated content-based image query system. ACM Multimedia, 87-98.
20. Vincent S et al. [2007]: Web Image Annotation by fusing visual features and textual information . SIGAPP’07,2007.
21. Zaher Al Aghbari [2009]: Effective image mining by representing color histograms as time series. Journal of Advanced Computational Intelligence and Intelligent Informatics, 13, 109-114.
22. Zaiane O et al.[1998]: Mining MultiMedia Data. CASCON’98: Meeting of Minds, Toronto, Canada, 83-96,.
23. Zhang Ji [2001]: An Information-driven framework for image mining. In : Proceedings of 12th International Conference on Database and Expert Systems Applications (DEXA), Munich, Germany.
24. Zhang Ji et al. [2001]: Image Mining: issues, frameworks and techniques.
25. In : Proceedings of the Second International Workshop on Multimedia
26. Data Mining (MDM/KDD’2001), San Francisco, CA, USA.