Search (3 results, page 1 of 1)

0.019828727 = product of:
  0.039657455 = sum of:
    0.039657455 = product of:
      0.07931491 = sum of:
        0.07931491 = weight(_text_:searching in 5944) [ClassicSimilarity], result of:
          0.07931491 = score(doc=5944,freq=4.0), product of:
            0.2091384 = queryWeight, product of:
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.051699217 = queryNorm
            0.37924606 = fieldWeight in 5944, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.046875 = fieldNorm(doc=5944)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

There are currently two dominant interface types for searching and browsing large image collections: keywordbased search, and searching by overall similarity to sample images. We present an alternative based on enabling users to navigate along conceptual dimensions that describe the images. The interface makes use of hierarchical faceted metadata and dynamically generated query previews. A usability study, in which 32 art history students explored a collection of 35,000 fine arts images, compares this approach to a standard image search interface. Despite the unfamiliarity and power of the interface (attributes that often lead to rejection of new search interfaces), the study results show that 90% of the participants preferred the metadata approach overall, 97% said that it helped them learn more about the collection, 75% found it more flexible, and 72% found it easier to use than a standard baseline system. These results indicate that a category-based approach is a successful way to provide access to image collections.

0.01168419 = product of:
  0.02336838 = sum of:
    0.02336838 = product of:
      0.04673676 = sum of:
        0.04673676 = weight(_text_:searching in 973) [ClassicSimilarity], result of:
          0.04673676 = score(doc=973,freq=2.0), product of:
            0.2091384 = queryWeight, product of:
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.051699217 = queryNorm
            0.22347288 = fieldWeight in 973, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.0390625 = fieldNorm(doc=973)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

XMage is introduced in this paper as a method for partial similarity searching in image databases. Region-based image retrieval is a method of retrieving partially similar images. It has been proposed as a way to accurately process queries in an image database. In region-based image retrieval, region matching is indispensable for computing the partial similarity between two images because the query processing is based upon regions instead of the entire image. A naive method of region matching is a sequential comparison between regions, which causes severe overhead and deteriorates the performance of query processing. In this paper, a new image contents representation, called Condensed eXtended Histogram (CXHistogram), is presented in conjunction with a well-defined distance function CXSim() on the CX-Histogram. The CXSim() is a new image-to-image similarity measure to compute the partial similarity between two images. It achieves the effect of comparing regions of two images by simply comparing the two images. The CXSim() reduces query space by pruning irrelevant images, and it is used as a filtering function before sequential scanning. Extensive experiments were performed on real image data to evaluate XMage. It provides a significant pruning of irrelevant images with no false dismissals. As a consequence, it achieves up to 5.9-fold speed-up in search over the R*-tree search followed by sequential scanning.

0.0052533974 = product of:
  0.010506795 = sum of:
    0.010506795 = product of:
      0.02101359 = sum of:
        0.02101359 = weight(_text_:22 in 4054) [ClassicSimilarity], result of:
          0.02101359 = score(doc=4054,freq=2.0), product of:
            0.18104185 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051699217 = queryNorm
            0.116070345 = fieldWeight in 4054, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0234375 = fieldNorm(doc=4054)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

6. 1.2011 11:22:12