T-IRS: textual query based image retrieval system for consumer photos

ABSTRACT

In this demonstration, we present a (quasi) real-time textual query based image retrieval system (T-IRS) for consumer photos by leveraging millions of web images and their associated rich textual descriptions (captions, categories, etc.). After a user provides a textual query (e.g., "boat"), our system automatically finds the positive web images that are related to the textual query "boat" as well as the negative web images which are irrelevant to the textual query. Based on these automatically retrieved positive and negative web images, we employ the decision stump ensemble classifier to rank personal consumer photos. To further improve the photo retrieval performance, we also develop a novel relevance feedback method, referred to as Cross-Domain Regularized Regression (CDRR), which effectively utilizes both the web images and the consumer images. Our system is inherently not limited by any predefined lexicon.

REFERENCES

• 1: R. Datta et al. Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys, 2008.
• 2: C. Fellbaum. WordNet: An Electronic Lexical Database. Bradford Books, 1998.
• 3: Y. Liu et al. Using Large-Scale Web Data to Facilitate Textual Query based Retrieval of Consumer Photos. ACM Multimedia, 2009.
• 4: X. Wang et al. Annotating images by mining image search results. T-PAMI, 2008.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

General Terms:
Algorithms, Experimentation, Performance

Keywords:
cross domain learning, text based photo retrieval