Relevance feedback is often a critical component when designing image databases. With these databases it is difficult to specify queries directly and explicitly. Relevance feedback interactively determinines a user’s desired output or query concept by asking the user whether certain proposed images are relevant or not. For a relevance feedback algorithm to be effective, it must grasp a user’s query concept accurately and quickly, while also only asking the user to label a small number of images. We propose the use of a support vector machine active learning algorithm for conducting effective relevance feedback for image retrieval. The algorithm selects the most informative images to query a user and quickly learns a boundary that separates the images that satisfy the user’s query concept from the rest of the dataset. Experimental results show that our algorithm achieves significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.

Specifying exact query concepts has become increasingly challenging to end-users. This is because many query concepts #e.g., those for looking up a multimedia object# can be hard to articulate, and articulation can be subjective. In this study,we propose a query-concept learner that learns query criteria through an intelligent sampling process. Our concept learner aims to ful#ll two primary design objectives: 1# it has to be expressive in order to model most practical query concepts, and 2# it must learn a concept quickly and with a small number of labeled data since online users tend to be too impatient to provide much feedback. To ful#ll the #rst goal, we model query concepts in k-CNF, which can express almost all practical query concepts. To ful#ll the second design goal, we propose our maximizing expected generalization algorithm #MEGA#, which converges to target concepts quickly by its two complementary steps: sample selection and concept re#nement. We also propose a divide-and-conquer method that divides the concept-learning task into G subtasks to achieve speedup. We notice that a task must be divided carefully, or search accuracy may su#er. Wethus employ a genetic-based mining algorithm to discover good feature groupings. Through analysis and mining results, we observe that organizing image features in a multi-resolution manner, and minimizing intragroup feature correlation, can speed up query-concept learning substantially while maintaining high search accuracy. Through examples, analysis, experiments, and an prototype implementation, we show that MEGA converges to query concepts signi#cantly faster than traditional methods. Keywords: query concept, relevance feedback, active learning, data mining. 1

To date, almost all research work in the Content-Based Image Retrieval (CBIR) community uses Minkowski-like functions to measure similarity between images. In this paper, we first present a non-metric distance function, dynamic partial function (DPF), which works significantly better than Minkowskilike functions for measuring perceptual similarity; and we explain DPF's link to similarity theories in cognitive science. We then propose DynDex, an indexing method that deals with both the dynamic and non-metric aspects of the distance function. DynDex employs statistical methods including distancebased classification and bagging to enable ecient indexing with DPF. In addition to its efficiency for conducting similarity searches in very high-dimensional spaces, we show that DynDex remains quite effective when features are weighted dynamically for supporting personalized searches.

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