Data preparation is the first and a very important step towards any Large Language Model (LLM) development. This paper introduces an easy-to-use, extensible, and scale-flexible open-source data preparation toolkit called Data Prep Kit (DPK). DPK is architected and designed to enable users to scale their data preparation to their needs. With DPK they can prepare data on a local machine or effortlessly scale to run on a cluster with thousands of CPU Cores. DPK comes with a highly scalable, yet extensible set of modules that transform natural language and code data. If the user needs additional transforms, they can be easily developed using extensive DPK support for transform creation. These modules can be used independently or pipelined to perform a series of operations. In this paper, we describe DPK architecture and show its performance from a small scale to a very large number of CPUs. The modules from DPK have been used for the preparation of Granite Models [1] [2]. We believe DPK is a valuable contribution to the AI community to easily prepare data to enhance the performance of their LLM models or to fine-tune models with Retrieval-Augmented Generation (RAG).

Object detectors often suffer from the domain gap between training (source domain) and real-world applications (target domain). Mean-teacher self-training is a powerful paradigm in unsupervised domain adaptation for object detection, but it struggles with low-quality pseudo-labels. In this work, we identify the intriguing alignment and synergy between mean-teacher self-training and contrastive learning. Motivated by this, we propose Contrastive Mean Teacher (CMT) - a unified, general-purpose framework with the two paradigms naturally integrated to maximize beneficial learning signals. Instead of using pseudo-labels solely for final predictions, our strategy extracts object-level features using pseudo-labels and optimizes them via contrastive learning, without requiring labels in the target domain. When combined with recent mean-teacher self-training methods, CMT leads to new state-of-the-art target-domain performance: 51.9% mAP on Foggy Cityscapes, outperforming the previously best by 2.1% mAP. Notably, CMT can stabilize performance and provide more significant gains as pseudo-label noise increases.

Predictions made by deep learning models are prone to data perturbations, adversarial attacks, and out-of-distribution inputs. To build a trusted AI system, it is there-fore critical to accurately quantify the prediction uncertainties. While current efforts focus on improving uncertainty quantification accuracy and efficiency, there is a need to identify uncertainty sources and take actions to mitigate their effects on predictions. Therefore, we propose to develop explainable and actionable Bayesian deep learning methods to not only perform accurate uncertainty quantification but also explain the uncertainties, identify their sources, and propose strategies to mitigate the uncertainty impacts. Specifically, we introduce a gradient-based uncertainty attribution method to identify the most problematic regions of the input that contribute to the prediction uncertainty. Compared to existing methods, the proposed UA-Backprop has competitive accuracy, relaxed assumptions, and high efficiency. Moreover, we propose an uncertainty mitigation strategy that leverages the attribution results as attention to further improve the model performance. Both qualitative and quantitative evaluations are conducted to demonstrate the effectiveness of our proposed methods.

The human visual perception system demonstrates exceptional capabilities in learning without explicit supervision and understanding the part-to-whole composition of objects. Drawing inspiration from these two abilities, we propose Hierarchical Adaptive Self-Supervised Object Detection (HASSOD), a novel approach that learns to detect objects and understand their compositions without human supervision. HASSOD employs a hierarchical adaptive clustering strategy to group regions into object masks based on self-supervised visual representations, adaptively determining the number of objects per image. Furthermore, HASSOD identifies the hierarchical levels of objects in terms of composition, by analyzing coverage relations between masks and constructing tree structures. This additional self-supervised learning task leads to improved detection performance and enhanced interpretability. Lastly, we abandon the inefficient multi-round self-training process utilized in prior methods and instead adapt the Mean Teacher framework from semi-supervised learning, which leads to a smoother and more efficient training process. Through extensive experiments on prevalent image datasets, we demonstrate the superiority of HASSOD over existing methods, thereby advancing the state of the art in self-supervised object detection. Notably, we improve Mask AR from 20.2 to 22.5 on LVIS, and from 17.0 to 26.0 on SA-1B. Project page: https://HASSOD-NeurIPS23.github.io.

Current methods for learning visually grounded language from videos often rely on text annotation, such as human generated captions or machine generated automatic speech recognition (ASR) transcripts. In this work, we introduce the Audio-Video Language Network (AVLnet), a self-supervised network that learns a shared audio-visual embedding space directly from raw video inputs. To circumvent the need for text annotation, we learn audio-visual representations from randomly segmented video clips and their raw audio waveforms. We train AVLnet on HowTo100M, a large corpus of publicly available instructional videos, and evaluate on image retrieval and video retrieval tasks, achieving state-of-the-art performance. We perform analysis of AVLnet's learned representations, showing our model utilizes speech and natural sounds to learn audio-visual concepts. Further, we propose a tri-modal model that jointly processes raw audio, video, and text captions from videos to learn a multi-modal semantic embedding space useful for text-video retrieval. Our code, data, and trained models will be released at avlnet.csail.mit.edu

Fine-grained action detection is an important task with numerous applications in robotics and human-computer interaction. Existing methods typically utilize a two-stage approach including extraction of local spatio-temporal features followed by temporal modeling to capture long-term dependencies. While most recent papers have focused on the latter (long-temporal modeling), here, we focus on producing features capable of modeling fine-grained motion more efficiently. We propose a novel locally-consistent deformable convolution, which utilizes the change in receptive fields and enforces a local coherency constraint to capture motion information effectively. Our model jointly learns spatio-temporal features (instead of using independent spatial and temporal streams). The temporal component is learned from the feature space instead of pixel space, e.g. optical flow. The produced features can be flexibly used in conjunction with other long-temporal modeling networks, e.g. ST-CNN, DilatedTCN, and ED-TCN. Overall, our proposed approach robustly outperforms the original long-temporal models on two fine-grained action datasets: 50 Salads and GTEA, achieving F1 scores of 80.22% and 75.39% respectively.

The production of sports highlight packages summarizing a game's most exciting moments is an essential task for broadcast media. Yet, it requires labor-intensive video editing. We propose a novel approach for auto-curating sports highlights, and demonstrate it to create a first of a kind, real-world system for the editorial aid of golf and tennis highlight reels. Our method fuses information from the players’ reactions (action recognition such as high-fives and fist pumps), players’ expressions (aggressive, tense, smiling, and neutral), spectators (crowd cheering), commentator (tone of the voice and word analysis), and game analytics to determine the most interesting moments of a game. We accurately identify the start and end frames of key shot highlights with additional metadata, such as the player's name and the whole number, or analysts input allowing personalized content summarization and retrieval. In addition, we introduce new techniques for learning our classifiers with reduced manual training data annotation by exploiting the correlation of different modalities. Our work has been demonstrated at a major golf tournament (2017 Masters) and two major international tennis tournaments (2017 Wimbledon and U.S. Open), successfully extracting highlights through the course of the sporting events. For the 2017 Masters, 54% of the clips selected by our system overlapped with the official highlights reels. Furthermore, user studies showed that 90% of the non-overlapping ones were of the same quality of the official clips for the 2017 Masters, while the automatic selection of clips for highlights of 2017 Wimbledon and 2017 US Open agreed with human preferences 80% and 84.2% of the time, respectively.

The availability of mobile access has shifted social media use. With that phenomenon, what users shared on social media and where they visited is naturally an excellent resource to learn their visiting behavior. Knowing visit behaviors would help market survey and customer relationship management, e.g., sending customers coupons of the businesses that they visit frequently. Most prior studies leverage meta-data e.g., check-in locations to profile visiting behavior but neglect important information from user-contributed content, e.g., images. This work addresses a novel use of image content for predicting the user visit behavior, i.e., the frequent and regular business venue categories that the content owner would visit. To collect training data, we propose a strategy to use geo-metadata associated with images for deriving the labels of an image owner's visit behavior. Moreover, we model a user's sequential images by using an end-to-end learning framework to reduce the optimization loss. That helps improve the prediction accuracy against the baseline as demonstrated in our experiments. The prediction is completely based on image content that is more available in social media than geo-metadata, and thus allows coverage in profiling a wider set of users.

We introduce a novel multi-modal system for auto-curating golf highlights that fuses information from players' reactions (celebration actions), spectators (crowd cheering), and commentator (tone of the voice and word analysis) to determine the most interesting moments of a game. The start of a highlight is determined with additional metadata (player's name and the hole number), allowing personalized content summarization and retrieval. Our system was demonstrated at Masters 2017, a major golf tournament, generating real-time highlights from four live video streams over four days.

In this paper, we describe the first-ever machine human collaboration at creating a real movie trailer (officially released by 20th Century Fox). We introduce an intelligent system designed to understand and encode patterns and types of emotions in horror movies that are useful in trailers. We perform multi-modal semantics extraction including audio visual sentiments and scene analysis and employ a statistical approach to model the key defining components that characterize horror movie trailers. The system was applied on a full-length feature film, "Morgan'' released in 2016 where the system identified 10 moments as best candidates for a trailer. We partnered with a professional filmmaker who arranged and edited each of the moments together to construct a comprehensive trailer completing the entire processing as well as the final trailer assembly within 24 hours. We discuss disruptive opportunities for the film industry and the tremendous media impact of the AI trailer. We confirm the effectiveness of our trailer with a very supportive user study. Finally based on our close interaction with the film industry, we also introduce and investigate the novel paradigm of tropes within the context of movies for advancing content creation.

Image localization is important for marketing and recommendation of local business; however, the level of granularity is still a critical issue. Given a consumer photo and its rough GPS information, we are interested in extracting the fine-grained location information, i.e. business venues, of the image. To this end, we propose a novel framework for business venue recognition. The framework mainly contains three parts. First, business-aware visual concept discovery: we mine a set of concepts that are useful for business venue recognition based on three guidelines including business awareness, visually detectable, and discriminative power. We define concepts that satisfy all of these three criteria as business-aware visual concept. Second, business-aware concept detection by convolutional neural networks (BA-CNN): we propose a new network configuration that can incorporate semantic signals mined from business reviews for extracting semantic concept features from a query image. Third, multimodal business venue recognition: we extend visually detected concepts to multimodal feature representations that allow a test image to be associated with business reviews and images from social media for business venue recognition. The experiments results show the visual concepts detected by BA-CNN can achieve up to 22.5% relative improvement for business venue recognition compared to the state-of-the-art convolutional neural network features. Experiments also show that by leveraging multimodal information from social media we can further boost the performance, especially when the database images belonging to each business venue are scarce.

Captions are a central component in image posts that communicate the background story behind photos. Captions can enhance the engagement with audiences and are therefore critical to campaigns or advertisement. Previous studies in image captioning either rely solely on image content or summarize multiple web documents related to image's location; both neglect users' activities. We propose business-aware latent topics as a new contextual cue for image captioning that represent user activities at business venues. The idea is to learn the typical activities of people who posted images from business venues with similar categories (e.g., fast food restaurants) to provide appropriate context for similar topics (e.g., burger) in new posts. User activities at businesses are modeled via a latent topic representation. In turn, the image captioning model can generate sentences that better reflect user activities at business venues. In our experiments, the business-aware latent topics are effective for adapting to captions to images captured in various businesses than the existing baselines. Moreover, they complement other contextual cues (image, time) in a multi-modal framework.

Knowing the geo-located venue of a tweet can facilitate better understanding of a user's geographic context, allowing apps to more precisely present information, recommend services, and target advertisements. However, due to privacy concerns, few users choose to enable geotagging of their tweets, resulting in a small percentage of tweets being geotagged; furthermore, even if the geo-coordinates are available, the closest venue to the geolocation may be incorrect. In this paper, we present a method for providing a ranked list of geo-located venues for a non-geotagged tweet, which simultaneously indicates the venue name and the geo-location at a very fine-grained granularity. In our proposed method for Venue Inference for Tweets (VIT), we construct a heterogeneous social network in order to analyze the embedded social relations, and leverage available but limited geographic data to estimate the geo-located venue of tweets. A single classifier is trained to estimate the probability of a tweet and a geo-located venue being linked, rather than training a separate model for each venue. We examine the performance of four types of social relation features and three types of geographic features embedded in a social network when inferring whether a tweet and a venue are linked, with a best accuracy of over 88%. We use the classifier probability estimates to rank the candidate geo-located venues of a non-geotagged tweet from over 19k possibilities, and observed an average top-5 accuracy of 29%.

Motivated by scalable partial-duplicate visual search, there has been growing interest in a wealth of compact and efficient binary feature descriptors (e.g. ORB, FREAK, BRISK). Typically, binary descriptors are clustered into codewords and quantized with Hamming distance, following the conventional bag-of-words strategy. However, such codewords formulated in Hamming space do not present obvious indexing and search performance improvement as compared to the Euclidean codewords. In this paper, without explicit codeword construction, we explore the use of partial binary descriptors as direct codebook indices (addresses). We propose a novel approach to build multiple index tables which concurrently check for collision of the same hash values. The evaluation is performed on two public image datasets: DupImage and Holidays. The experimental results demonstrate the indexing efficiency and retrieval accuracy of our approach.

We propose Multi-modal Language Models (MLMs), which adapt latent variable techniques for document analysis to exploring co-occurrence relationships in multi-modal data. In this paper, we focus on the application of MLMs to indexing text from slides and speech in lecture videos, and subsequently employ a multi-modal probabilistic ranking function for lecture video retrieval. The MLM achieves highly competitive results against well established retrieval methods such as the Vector Space Model and Probabilistic Latent Semantic Analysis. When noise is present in the data, retrieval performance with MLMs is shown to improve with the quality of the spoken text extracted from the video.

Social multimedia sharing and hosting websites, such as Flickr and Facebook, contain billions of user-submitted images. Popular Internet commerce websites such as Amazon.com are also furnished with tremendous amounts of product-related images. In addition, images in such social networks are also accompanied by annotations, comments, and other information, thus forming heterogeneous image-rich information networks. In this paper, we introduce the concept of (heterogeneous) image-rich information network and the problem of how to perform information retrieval and recommendation in such networks. We propose a fast algorithm heterogeneous minimum order k-SimRank (HMok-SimRank) to compute link-based similarity in weighted heterogeneous information networks. Then, we propose an algorithm Integrated Weighted Similarity Learning (IWSL) to account for both link-based and content-based similarities by considering the network structure and mutually reinforcing link similarity and feature weight learning. Both local and global feature learning methods are designed. Experimental results on Flickr and Amazon data sets show that our approach is significantly better than traditional methods in terms of both relevance and speed. A new product search and recommendation system for e-commerce has been implemented based on our algorithm.

Tag-clouds are becoming extremely popular in multimedia community as media of exploration and expression. In this work, we take tag-cloud construction to a new level by allowing a tag-cloud to take any arbitrary shape while preserving some order of tags (here alphabetical). Our method guarantees non-overlap among words and ensures compact representation within specified shape. The experiments on a variety of input set of tags and shapes of the tag-clouds show that the proposed method is promising and has real-time performance. Finally, we show the applicability of our method with an application wherein the tag-clouds specific to places, people, and keywords are constructed and used for digital media selection within a social network domain.

In recent years, several methods have been proposed to exploit image context (such as location) that provides valuable cues complementary to the image content, i.e., image annotations and geotags have been shown to assist the prediction of each other. To exploit the useful interrelatedness between these two heterogeneous prediction tasks, we propose a new correlation guided structured sparse multi-task learning method. We utilize a joint classification and regression model to identify annotation-informative and geotag-relevant image features. We also introduce the tree-structured sparsity regularizations into multi-task learning to integrate the label correlations in multi-label image annotation. Finally we derive an efficient algorithm to optimize our non-smooth objective function. We demonstrate the performance of our method on three real-world geotagged multi-label image data sets for both semantic annotation and geotag prediction.

We demonstrate a system to generate dynamic media shows that are significantly richer than static slide shows, which are currently the most popular form of photo playback. The goal is to enable media reliving experiences that are aesthetically pleasing, interactive, and semantically drivable as they center on people, locations, time, and events discovered in a media collection. Dynamic shows allow for better sharing of one's media collections in diverse social networks because people have different time availabilities and perspectives, and hence may want to interact, customize, and reroute the media flow per their individual needs.

Billions of people worldwide use images and videos to capture various events in their lives. The primary purpose of the proposed media sharing application is digital re-living of those events by the photographers and their families and friends. The most popular tools for achieving this today are still static slide-shows (SSS) which primarily focus on visual effects rather than understanding the semantics of the media assets being used, or allowing different viewers (e.g. friends, family, who have different relationships, interests, time availabilities, and familiarities) any control over the flow of the show. We present a novel system that generates an aesthetically appealing and semantically drivable audio-visual media show based on several reliving dimensions of events, people, locations, and time. We allow each viewer to interact with the default presentation to 'on-the-fly' redirect the flow of reliving as desired from their individual perspectives. Moreover, each reliving session is logged and can be shared with other people over a wide array of platforms and devices, allowing sharing experience to go beyond the sharing of the media assets themselves. From a detailed analysis of the logged sessions across different user categories, we have obtained many interesting findings on the reliving needs, behaviors and patterns, which in turn validate our design motivations and principles.

Geotagging has become a recent phenomenon that allows users to visualize and manage photo collections in many new and interesting ways. Unfortunately, manual geotagging of a large collection of pictures on the globe is still a time-consuming and laborious task even though geotagging devices are gradually being adopted. At the same time, users often provide tag annotations, which may contain useful geographic cues, for their pictures. In this paper, we explore using annotations for inferring the location of images. Using a collection of over a million geotagged pictures, we build location probability maps for tag annotations over the entire globe. These maps reflect the collective picture-taking and tagging behaviors of thousands of users from all over the world. We study the geographic entropy and frequency of user tags as geo-inference features and investigate the usefulness of using these features for selecting geographically meaningful annotations in a probabilistic framework. We show that the geo-distribution of a tag relates to the semantic meaning of the tag itself and we can effectively determine which tags are cities or nations by examining the tag maps themselves. Furthermore, user annotations alone can be used to infer the location of pictures with good accuracy.

In this paper, we describe a method for matching song lyrics with semantic annotations of picture collections in order to suggest songs that reflect picture content in lyrics or genre. Picture collections are first analyzed to extract a variety of semantic information including scene type, event type, and geospatial information. When aggregated over a picture collection, this semantic information forms a semantic signature of the collection. Typical picture collections in our scenario consist of photo subdirectories in which people store pictures of a place, activity, or event. Picture collections are expected to contain coherent semantic content describing in part or whole the event or activity they depict. The semantic signature of a picture collection is compared against song lyrics using a WordNet expansion based text matching to find songs relevant to the collection. We present interesting song suggestions, compare and contrast scenarios with human versus machine labels, and perform a user study to validate the usefulness of the proposed method. The proposed method will be a useful tool to support user media creation.

In this demo, we present a system called iRIN designed for performing image retrieval in image-rich information networks. We first introduce MoK-SimRank to significantly improve the speed of SimRank, one of the most popular algorithms for computing node similarity in information networks. Next, we propose an algorithm called SimLearn to (1) extend MoK-SimRank to heterogeneous image-rich information network, and (2) account for both link-based and content-based similarities by seamlessly integrating reinforcement learning with feature learning.

Popular photo-sharing sites have attracted millions of people and helped construct massive social networks in Cyberspace. Different from traditional social relationships, users actively interact in groups where common interests are shared on certain types of events or topics captured by photos and videos. Contributing images to an interest group would greatly promote interactions between users and expand their social networks. In this work, we intend to produce automatic recommendations of a user's images to suitable photo-sharing groups. To this end, we begin with analyzing user annotations and modeling the shared images in a group. Both visual content and annotation context are then integrated to understand the events or topics depicted in those images. Experiments on over 14000 user images demonstrate the feasibility of the proposed approach.

Semantic event recognition based only on vision cues has had limited success on unconstrained still pictures. Metadata related to picture taking provides contextual cues independent of the image content and can be used to improve classification performance. With geotagged pictures, we investigate the novel use of satellite images (e.g., supplied by Google Earth™) corresponding to the GPS (Global Positioning System) coordinates to recognize a picture-taking environment. Initial assessment reveals that with minimum training humans can achieve high accuracy in the recognition of terrain environment based on views from above. We propose to employ both color- and structure-based vocabularies for characterizing satellite images in terms of 14 of the most interesting classes, including residential areas, commercial areas, sports venues, parks, and schools. A multiclass AdaBoost engine is trained to predict terrain environment given a satellite aerial image of the location. Initial experimental results using two-fold cross-validation have shown promise for employing the proposed scheme for event recognition.

Semantic event recognition based only on vision cues is a challenging problem. This problem is particularly acute when the application domain is unconstrained still images available on the Internet or in personal repositories. In recent years, it has been shown that metadata captured with pictures can provide valuable contextual cues complementary to the image content and can be used to improve classification performance. With the recent geotagging phenomenon, an important piece of metadata available with many geotagged pictures now on the World Wide Web is GPS information. In this study, we obtain satellite images corresponding to picture location data and investigate their novel use to recognize the picture-taking environment, as if through a third eye above the object. Additionally, we combine this inference with classical vision-based event detection methods and study the synergistic fusion of the two approaches. We employ both color- and structure-based visual vocabularies for characterizing ground and satellite images, respectively. Training of satellite image classifiers is done using a multiclass AdaBoost engine while the ground image classifiers are trained using SVMs. Modeling and prediction involve some of the most interesting semantic event-activity classes encountered in consumer pictures, including those that occur in residential areas, commercial areas, beaches, sports venues, and parks. The powerful fusion of the complementary views achieves significant performance improvement over the ground view baseline. With integrated GPS-capable cameras on the horizon, we believe that our line of research can revolutionize event recognition and media annotation in years to come.

We propose a greedy performance driven algorithm for learning how to fuse across multiple classification and search systems. We assume a scenario when many such systems need to be fused to generate the final ranking. The algorithm is inspired from Ensemble Learning but takes that idea further for improving generalization capability. Fusion learning is applied to leverage text, visual and model based modalities for 2005 TRECVID query retrieval task. Experiments using the well established retrieval effectiveness measure of mean average precision reveal that our proposed algorithm improves over naive baseline (fusion with equal weights) as well as over Caruana's original algorithm (NACHOS) by 36% and 46% respectively.

Automatic image annotation has been a hot-pursuit among multimedia researchers of late. Modest performance guarantees and limited adaptability often restrict its applicability to real-world settings. We propose tagging over time (T/T) to push the technology toward real-world applicability. Of particular interest are online systems that receive user-provided images and feedback over time, with user focus possibly changing and evolving. The T/T framework consists of a principled probabilistic approach to meta-learning, which acts as a go-between for a 'black-box' annotation system and the users. Inspired by inductive transfer, the approach attempts to harness available information, including the black-box model's performance, the image representations, and the WordNet ontology. Being computationally 'lightweight', this meta-learner efficiently re-trains over time, to improve and/or adapt to changes. The black-box annotation model is not required to be re-trained, allowing computationally intensive algorithms to be used. We experiment with standard image datasets and real-world data streams, using two existing annotation systems as black-boxes. Both batch and online annotation settings are experimented with. It is observed that the addition of this meta-learning layer produces much improved results that outperform best-known results. For the online setting, the T/T approach produces progressively better annotation with time, significantly outperforming the black-box as well as the static form of the meta-learner, on real-world data.

Statistical modeling methods are becoming indispensable in today's large-scale image analysis. In this paper, we explore a computationally efficient parameter estimation algorithm for two-dimensional (2-D) and three-dimensional (3-D) hidden Markov models (HMMs) and show applications to satellite image segmentation. The proposed parameter estimation algorithm is compared with the first proposed algorithm for 2-D HMMs based on variable state Viterbi. We also propose a 3-D HMM for volume image modeling and apply it to volume image segmentation using a large number of synthetic images with ground truth. Experiments have demonstrated the computational efficiency of the proposed parameter estimation technique for 2-D HMMs and a potential of 3-D HMM as a stochastic modeling tool for volume images.

We present an unsupervised approach to automated story picturing. Semantic keywords are extracted from the story, an annotated image database is searched. Thereafter, a novel image ranking scheme automatically determines the importance of each image. Both lexical annotations and visual content play a role in determining the ranks. Annotations are processed using the Wordnet. A mutual reinforcement-based rank is calculated for each image. We have implemented the methods in our Story Picturing Engine (SPE) system. Experiments on large-scale image databases are reported. A user study has been performed and statistical analysis of the results has been presented.

Parameter estimation is a key computational issue in all statistical image modeling techniques. In this paper, we explore a computationally efficient parameter estimation algorithm for multi-dimensional hidden Markov models. 2-D HMM has been applied to supervised aerial image classification and comparisons have been made with the first proposed estimation algorithm. An extensive parametric study has been performed with 3-D HMM and the scalability of the estimation algorithm has been discussed. Results show the great applicability of the explored algorithm to multi-dimensional HMM based image modeling applications.

Over the years, researchers in the image analysis community have successfully used various statistical modeling methods to segment, classify and annotate digital images. In this paper, we propose a 3-D hidden Markov model (HMM) for volume image modeling. A computationally efficient algorithm is developed to estimate the model. The 3-D HMM is applied to volume image segmentation and tested using synthetic images with ground truth. Experiments have demonstrated that 3-D HMM outperforms Gaussian mixture model based clustering by an order of magnitude in accuracy.

Due to an increasing interest in solving real-world optimization problems using evolutionary algorithms (EAs), researchers have developed a number of real-parameter genetic algorithms (GAs) in the recent past. In such studies, the main research effort is spent on developing an efficient recombination operator. Such recombination operators use probability distributions around the parent solutions to create offspring. Some operators emphasize solutions at the center of mass of parents and some around the parents. We propose a generic parent-centric recombination operator (PCX) and compare its performance with a couple of commonly-used mean-centric recombination operators (UNDX and SPX). With the help of a steady-state, elite-preserving, and computationally fast EA model, simulation results show the superiority of PCX on three test problems.

Due to increasing interest in solving real-world optimization problems using evolutionary algorithms (EAs), researchers have recently developed a number of real-parameter genetic algorithms (GAs). In these studies, the main research effort is spent on developing an efficient recombination operator. Such recombination operators use probability distributions around the parent solutions to create an offspring. Some operators emphasize solutions at the center of mass of parents and some around the parents. In this paper, we propose a generic parent-centric recombination operator (PCX) and a steady-state, elite-preserving, scalable, and computationally fast population-alteration model (we call the G3 model). The performance of the G3 model with the PCX operator is investigated on three commonly used test problems and is compared with a number of evolutionary and classical optimization algorithms including other real-parameter GAs with the unimodal normal distribution crossover (UNDX) and the simplex crossover (SPX) operators, the correlated self-adaptive evolution strategy, the covariance matrix adaptation evolution strategy (CMA-ES), the differential evolution technique, and the quasi-Newton method. The proposed approach is found to consistently and reliably perform better than all other methods used in the study. A scale-up study with problem sizes up to 500 variables shows a polynomial computational complexity of the proposed approach. This extensive study clearly demonstrates the power of the proposed technique in tackling real-parameter optimization problems.