Mandar Mitra

Image indexing using color correlograms

We define a new image feature called the color correlogram and use it for image indexing and comparison. This feature distills the spatial correlation of colors, and is both effective and inexpensive for content-based image retrieval. The correlogram robustly tolerates large changes in appearance and shape caused by changes in viewing positions, camera zooms, etc. Experimental evidence suggests that this new feature outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval.

Improving automatic query expansion

Most casual users of IR systems type short queries. Recent research has shown that adding new words to these queries via odhoc feedback improves the retrieval effectiveness of such queries. We investigate ways to improve this query expansion process by refining the set of documents used in feedback. We start by using manually formulated Boolean filters along with proximity constraints. Our approach is similar to the one proposed by Hearst[l2]. Next, we investigate a completely automatic method that makes use of term cooccurrence information to estimate word correlation. Experimental results show that refining the set of documents used in query expansion often prevents the query drift caused by blind expansion and yields substantial improvements in retrieval effectiveness, both in terms of average precision and precision in the top twenty documents. More importantly, the fully automatic approach developed in this study performs competitively with the best manual approach and requires little computational overhead.

Automatic text structuring and summarization

Abstract In recent years, information retrieval techniques have been used for automatic generation of semantic hypertext links. This study applies the ideas from the automatic link generation research to attack another important problem in text processing—automatic text summarization. An automatic “general purpose” text summarization tool would be of immense utility in this age of information overload. Using the techniques used (by most automatic hypertext link generation algorithms) for inter-document link generation, we generate intra-document links between passages of a document. Based on the intra-document linkage pattern of a text, we characterize the structure of the text. We apply the knowledge of text structure to do automatic text summarization by passage extraction. We evaluate a set of fifty summaries generated using our techniques by comparing them to paragraph extracts constructed by humans. The automatic summarization methods perform well, especially in view of the fact that the summaries generated by two humans for the same article are surprisingly dissimilar.

Spatial Color Indexing and Applications

We define a new image feature called the color correlogram and use it for image indexing and comparison. This feature distills the spatial correlation of colors and when computed efficiently, turns out to be both effective and inexpensive for content-based image retrieval. The correlogram is robust in tolerating large changes in appearance and shape caused by changes in viewing position, camera zoom, etc. Experimental evidence shows that this new feature outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval. We also provide a technique to cut down the storage requirement of the correlogram so that it is the same as that of histograms, with only negligible performance penalty compared to the original correlogram.We also suggest the use of color correlogram as a generic indexing tool to tackle various problems arising from image retrieval and video browsing. We adapt the correlogram to handle the problems of image subregion querying, object localization, object tracking, and cut detection. Experimental results again suggest that the color correlogram is more effective than the histogram for these applications, with insignificant additional storage or processing cost.

Document length normalization

In the TREC collection -a large full-text experimental text collection with widely varying document lengths -we observe that the likelihood of a document being judged relevant by a user increases with the document length. We show that a retrieval strategy, such as the vector-space cosine match, that retrieves documents of different lengths with roughly equal probability, will not optimally retrieve useful documents from such a collection. We present a modified technique that attempts to match the likelihood of retrieving a document of a certain length to the likelihood of documents of that length being judged relevant, and show that this technique yields significant improvements in retrieval effectiveness.

Automatic text decomposition using text segments and text themes

With the widespread use of full-text information retrieval, passage-retrieval techniques are becoming increasingly popular. Larger texts can then be replaced by important text excerpts, thereby simplifying the retrieval task and improving retrieval effectiveness. Passage-level evidence about the use of words in local contexts is also useful for resolving language ambiguities and improving retrieval output. Two main text decomposition strategies are introduced in this study, including a chronological decomposition into {em text segments}, and semantic decomposition into {em text themes}. The interaction between text segments and text themes is then used to characterize text structure, and to formulate specifications for information retrieval, text traversal, and text summarization.

Combining supervised learning with color correlograms for content-based image retrieval

The paper addresses how relevance feedback can be used to improve the performance of content-based image retrieval. We present two supervised learning methods: learning the query and learning the metric. We combine the learning methods with the recently proposed color correlograms for image indexing/retrieval. Our results on a large image database of over 20; 000 images suggest that these learning methods are quite effective for content-based image retrieval.

Learning routing queries in a query zone

Word usage is domain dependent. A common word in one domain can be quite infrequent in another. In this study we exploit th~ property of word usage to improve document routing. We show that routing queries (profiles) learned only from the documents in a query domain are better than the routing profiles learned when query domains are not used. We approximate a query domain by a guerg zone. Experiments show that routing profiles learned from a query zone are 8–12~0 more effective than the profiles generated when no query zoning is used.

Using clustering and SuperConcepts within SMART: TREC 6

Abstract The SMART information retrieval project emphasizes completely automatic approaches to the understanding and retrieval of large quantities of text. We continue our work in TREC 6, performing runs in the routing, ad hoc, and foreign language environments, including cross-lingual runs. The major focus for TREC 6 is on trying to maintain the balance of the query — attempting to ensure the various aspects of the original query are appropriately addressed, especially while adding expansion terms. Exactly the same procedure is used for foreign language environments as for English; our tenet is that good information retrieval techniques are more powerful than linguistic knowledge. We also give an interesting cross-lingual run, assuming that French and English are closely enough related so that a query in one language can be run directly on a collection in the other language by just ‘correcting’ the spelling of the query words. This is quite successful for most queries.

Spatial color indexing and applications

We suggest the use of the color correlogram as a generic indexing tool to tackle various computer vision problems. Correlograms were shown to be very effective for content-based image retrieval. We adapt the correlogram to handle the problems of image subregion querying, object localization, object tracking, and cut detection. Experimental results suggest that the color correlogram is much more effective than the histogram for these applications, with insignificant additional computational, storage, or processing cost. We also provide a technique to cut down the storage requirement of correlograms so that it is the same as that of histograms, with only negligible performance penalty compared to the original correlogram.