Image retrieval has been extensively studied over the last two decades due to the increasing demands for the effective use of multimedia data. Among various approaches to image retrieval, scale space representation and local keypoint descriptors have been shown to be a promising approach. Even though the concept of scale space representation has been known for a long time, it has now gained prominence as a powerful method for image retrieval mostly due to the invention of the Scale Invariant Feature Transform (SIFT). We will review the characteristics of the scale space operation and provide an extended method of scale space operation that significantly improves the image matching accuracy in the context of image retrieval. We use an operational tattoo image database containing 1,000 near duplicate images to show the superior retrieval performance of the proposed method compared to SIFT keypoints.

We introduce an alternative structure for computing the a posteriori probabilities (APPs) for state and transition sequences of a Markov source observed through a noisy output sequence. Compared to the well-established forward-backward recursion algorithm of Bahl et al. (1974), the proposed structure allows a reduction in computational complexity at the expense of increased memory requirements. Alternatively, for a similar complexity level, the proposed structure needs smaller memory when the input alphabet size is small.

We develop a new method for simple soft-output detection. Like other soft-output detectors, our approach also utilizes the trellis structure. However, in computing the state transition probabilities in the trellis, we model each bit as a linear regression of the channel outputs as well as the previous bits implied in the corresponding branch. The trellis depends not on the channel response but on the regression model parameter that is highly flexible. The resulting soft-output algorithm has a very simple structure, needing only a forward recursion. The amount of memory required is also small. Furthermore, this algorithm can easily handle colored/signal-dependent noise. We examine the performance of this algorithm for three ISI channels with different noise characteristics: additive white, colored, and colored and signal-dependent. To verify the quality of the soft decisions, an outer low-density parity check code is used. Results show that the proposed detector allows favorable complexity/performance tradeoffs in correlated/signal-dependent noise environment.

Maximum and near-maximum likelihood sequence detectors in signal-dependent noise are discussed. It is shown that the linear prediction viewpoint allows a very simple derivation of the branch metric expression that has previously been shown as optimum for signal-dependent Markov noise. The resulting detector architecture is viewed as a noise predictive maximum likelihood detector that operates on an expanded trellis and relies on computation of branch-specific, pattern-dependent noise predictor taps and predictor error variances. Comparison is made on the performance of various low-complexity structures using the positional-jitter/width-variation model for transition noise. It is shown that when medium noise dominates, a reasonably low complexity detector that incorporates pattern-dependent noise prediction achieves a significant signal-to-noise ratio gain relative to the extended class 4 partial response maximum likelihood detector. Soft-output detectors as well as the use of soft decision feedback are discussed in the context of signal-dependent noise.