TAILIEUCHUNG - Multimedia_Data_Mining_07

Chapter 7 (MDM) | Chapter 6 Image Database Modeling – Latent Semantic Concept Discovery Introduction This chapter addresses image database modeling in general and, in particu- lar, focuses on developing a hidden semantic concept discovery methodology to address effective semantics-intensive image data mining and retrieval. In the approach proposed in this chapter, each image in the database is segmented into regions associated with homogenous color, texture, and shape features. By exploiting regional statistical information in each image and employing a vector quantization method, a uniform and sparse region-based represen- tation is achieved. With this representation a probabilistic model based on the statistical-hidden-class assumptions of the image database is obtained, to which the Expectation-Maximization (EM) technique is applied to discover and analyze semantic concepts hidden in the database. An elaborated min- ing and retrieval algorithm is designed to support the probabilistic model. The semantic similarity is measured through integrating the posterior prob- abilities of the transformed query image, as well as a constructed negative example, to the discovered semantic concepts. The proposed approach has a solid statistical foundation; the experimental evaluations on a database of 10,000 general-purpose images demonstrate the promise and the effectiveness of the proposed approach. The rest of this chapter is organized as follows. Section gives back- ground information regarding why it is necessary to propose and develop the latent semantic concept discovery approach to model an image database and reviews the related work in the literature. Section introduces the region feature extraction method and the region based image representation scheme used in developing this latent semantic concept discovery approach. Sec- tion then presents the proposed probabilistic region–image–concept model and the hidden semantic concept discovery procedure using the Expectation-

• Cơ sở dữ liệu
• Multimedia Data Mining (Ch 7)